{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c859df12",
   "metadata": {},
   "source": [
    "# V2 — corrected experiments and results\n",
    "\n",
    "This notebook documents and loads the completed 72-run grid. It is separate from all original notebooks. The corrected runs used standalone scripts; the outputs below load their saved results rather than pretending the historical notebooks were rerun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "42a9b546",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-20T16:41:45.887855Z",
     "iopub.status.busy": "2026-09-20T16:41:45.887682Z",
     "iopub.status.idle": "2026-09-20T16:41:46.153339Z",
     "shell.execute_reply": "2026-09-20T16:41:46.152635Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canonical results: /Users/joaoquintanilha/Downloads/data_297_final_paper/results_v2\n",
      "Training is disabled by default; existing artifacts are read below.\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import json, os, subprocess, sys\n",
    "import pandas as pd\n",
    "from IPython.display import display, Image, Markdown\n",
    "ROOT = Path.cwd()\n",
    "if not (ROOT / 'scripts_v2').is_dir():\n",
    "    ROOT = ROOT.parent\n",
    "assert (ROOT / 'scripts_v2').is_dir(), 'Open this notebook from the project or notebooks_v2 folder.'\n",
    "RESULTS = ROOT / 'results_v2'\n",
    "PYTHON = ROOT / '.venv' / 'bin' / 'python'\n",
    "sys.path.insert(0, str(ROOT / 'scripts_v2'))\n",
    "ENV = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='1')\n",
    "def command(script, *args):\n",
    "    result = subprocess.run([str(PYTHON), str(ROOT / 'scripts_v2' / script), *args],\n",
    "                            cwd=ROOT, env=ENV, capture_output=True, text=True)\n",
    "    print(result.stdout)\n",
    "    if result.stderr:\n",
    "        print(result.stderr)\n",
    "    result.check_returncode()\n",
    "print('Canonical results:', RESULTS)\n",
    "print('Training is disabled by default; existing artifacts are read below.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9023185",
   "metadata": {},
   "source": [
    "## Optional reproduction\n",
    "\n",
    "All switches default to False. Matching completed results are skipped by the runner; changing the experimental design requires a new output version. The recorded output alias `results/review_revision_v1` resolves to `results_v2` so original training hashes stay valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d93876c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-20T16:41:46.154736Z",
     "iopub.status.busy": "2026-09-20T16:41:46.154548Z",
     "iopub.status.idle": "2026-09-20T16:41:46.157545Z",
     "shell.execute_reply": "2026-09-20T16:41:46.156976Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No preprocessing or training launched.\n"
     ]
    }
   ],
   "source": [
    "RUN_PREPROCESS = False\n",
    "RUN_BASELINES = False\n",
    "RUN_NEURAL = False\n",
    "if RUN_PREPROCESS:\n",
    "    command('revision_data.py')\n",
    "if RUN_BASELINES:\n",
    "    command('revision_experiments.py', '--stage', 'baselines')\n",
    "if RUN_NEURAL:\n",
    "    command('revision_experiments.py', '--stage', 'neural')\n",
    "if not any([RUN_PREPROCESS, RUN_BASELINES, RUN_NEURAL]):\n",
    "    print('No preprocessing or training launched.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b015621",
   "metadata": {},
   "source": [
    "## Completed grid\n",
    "\n",
    "The grid contains 12 representations × 2 split protocols × 3 runs/folds. The two-epoch smoke run is excluded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f83c6845",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-20T16:41:46.158744Z",
     "iopub.status.busy": "2026-09-20T16:41:46.158622Z",
     "iopub.status.idle": "2026-09-20T16:41:46.175356Z",
     "shell.execute_reply": "2026-09-20T16:41:46.174799Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"expected_runs\": 72,\n",
      "  \"completed_runs\": 72,\n",
      "  \"missing\": []\n",
      "}\n",
      "random\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>task</th>\n",
       "      <th>coda_type</th>\n",
       "      <th>individual_id</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>0.253 ± 0.028</td>\n",
       "      <td>0.491 ± 0.029</td>\n",
       "      <td>0.910 ± 0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES_Adapter</th>\n",
       "      <td>0.272 ± 0.027</td>\n",
       "      <td>0.479 ± 0.018</td>\n",
       "      <td>0.933 ± 0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES_ICI_Adapter</th>\n",
       "      <td>0.452 ± 0.023</td>\n",
       "      <td>0.582 ± 0.078</td>\n",
       "      <td>0.940 ± 0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BirdNET</th>\n",
       "      <td>0.215 ± 0.053</td>\n",
       "      <td>0.480 ± 0.022</td>\n",
       "      <td>0.887 ± 0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCCE_ID</th>\n",
       "      <td>0.722 ± 0.051</td>\n",
       "      <td>0.588 ± 0.079</td>\n",
       "      <td>0.889 ± 0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCCE_no_ID</th>\n",
       "      <td>0.800 ± 0.026</td>\n",
       "      <td>0.627 ± 0.057</td>\n",
       "      <td>0.907 ± 0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCCE_swap_ID</th>\n",
       "      <td>0.636 ± 0.042</td>\n",
       "      <td>0.641 ± 0.066</td>\n",
       "      <td>0.879 ± 0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICI_Temporal_Mel</th>\n",
       "      <td>0.804 ± 0.016</td>\n",
       "      <td>0.724 ± 0.039</td>\n",
       "      <td>0.837 ± 0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raw_ICI</th>\n",
       "      <td>0.987 ± 0.009</td>\n",
       "      <td>0.439 ± 0.007</td>\n",
       "      <td>0.570 ± 0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supervised_Dual</th>\n",
       "      <td>0.762 ± 0.119</td>\n",
       "      <td>0.684 ± 0.081</td>\n",
       "      <td>0.886 ± 0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal_Mel</th>\n",
       "      <td>0.202 ± 0.025</td>\n",
       "      <td>0.429 ± 0.045</td>\n",
       "      <td>0.773 ± 0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WhAM</th>\n",
       "      <td>0.258 ± 0.046</td>\n",
       "      <td>0.450 ± 0.052</td>\n",
       "      <td>0.906 ± 0.011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "task                  coda_type  individual_id           unit\n",
       "model                                                        \n",
       "AVES              0.253 ± 0.028  0.491 ± 0.029  0.910 ± 0.010\n",
       "AVES_Adapter      0.272 ± 0.027  0.479 ± 0.018  0.933 ± 0.009\n",
       "AVES_ICI_Adapter  0.452 ± 0.023  0.582 ± 0.078  0.940 ± 0.009\n",
       "BirdNET           0.215 ± 0.053  0.480 ± 0.022  0.887 ± 0.020\n",
       "DCCE_ID           0.722 ± 0.051  0.588 ± 0.079  0.889 ± 0.004\n",
       "DCCE_no_ID        0.800 ± 0.026  0.627 ± 0.057  0.907 ± 0.003\n",
       "DCCE_swap_ID      0.636 ± 0.042  0.641 ± 0.066  0.879 ± 0.029\n",
       "ICI_Temporal_Mel  0.804 ± 0.016  0.724 ± 0.039  0.837 ± 0.013\n",
       "Raw_ICI           0.987 ± 0.009  0.439 ± 0.007  0.570 ± 0.023\n",
       "Supervised_Dual   0.762 ± 0.119  0.684 ± 0.081  0.886 ± 0.017\n",
       "Temporal_Mel      0.202 ± 0.025  0.429 ± 0.045  0.773 ± 0.007\n",
       "WhAM              0.258 ± 0.046  0.450 ± 0.052  0.906 ± 0.011"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_grouped\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>task</th>\n",
       "      <th>coda_type</th>\n",
       "      <th>individual_id</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AVES</th>\n",
       "      <td>0.061 ± 0.022</td>\n",
       "      <td>0.074 ± 0.051</td>\n",
       "      <td>0.700 ± 0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES_Adapter</th>\n",
       "      <td>0.079 ± 0.029</td>\n",
       "      <td>0.059 ± 0.044</td>\n",
       "      <td>0.646 ± 0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVES_ICI_Adapter</th>\n",
       "      <td>0.183 ± 0.037</td>\n",
       "      <td>0.081 ± 0.072</td>\n",
       "      <td>0.669 ± 0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BirdNET</th>\n",
       "      <td>0.063 ± 0.011</td>\n",
       "      <td>0.085 ± 0.022</td>\n",
       "      <td>0.612 ± 0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCCE_ID</th>\n",
       "      <td>0.502 ± 0.018</td>\n",
       "      <td>0.257 ± 0.063</td>\n",
       "      <td>0.574 ± 0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCCE_no_ID</th>\n",
       "      <td>0.616 ± 0.066</td>\n",
       "      <td>0.281 ± 0.109</td>\n",
       "      <td>0.687 ± 0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCCE_swap_ID</th>\n",
       "      <td>0.476 ± 0.084</td>\n",
       "      <td>0.219 ± 0.089</td>\n",
       "      <td>0.611 ± 0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICI_Temporal_Mel</th>\n",
       "      <td>0.668 ± 0.051</td>\n",
       "      <td>0.255 ± 0.062</td>\n",
       "      <td>0.608 ± 0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raw_ICI</th>\n",
       "      <td>0.956 ± 0.036</td>\n",
       "      <td>0.379 ± 0.103</td>\n",
       "      <td>0.540 ± 0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supervised_Dual</th>\n",
       "      <td>0.675 ± 0.058</td>\n",
       "      <td>0.270 ± 0.095</td>\n",
       "      <td>0.611 ± 0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temporal_Mel</th>\n",
       "      <td>0.069 ± 0.012</td>\n",
       "      <td>0.130 ± 0.074</td>\n",
       "      <td>0.569 ± 0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WhAM</th>\n",
       "      <td>0.066 ± 0.016</td>\n",
       "      <td>0.101 ± 0.075</td>\n",
       "      <td>0.667 ± 0.155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "task                  coda_type  individual_id           unit\n",
       "model                                                        \n",
       "AVES              0.061 ± 0.022  0.074 ± 0.051  0.700 ± 0.100\n",
       "AVES_Adapter      0.079 ± 0.029  0.059 ± 0.044  0.646 ± 0.134\n",
       "AVES_ICI_Adapter  0.183 ± 0.037  0.081 ± 0.072  0.669 ± 0.119\n",
       "BirdNET           0.063 ± 0.011  0.085 ± 0.022  0.612 ± 0.133\n",
       "DCCE_ID           0.502 ± 0.018  0.257 ± 0.063  0.574 ± 0.074\n",
       "DCCE_no_ID        0.616 ± 0.066  0.281 ± 0.109  0.687 ± 0.023\n",
       "DCCE_swap_ID      0.476 ± 0.084  0.219 ± 0.089  0.611 ± 0.014\n",
       "ICI_Temporal_Mel  0.668 ± 0.051  0.255 ± 0.062  0.608 ± 0.150\n",
       "Raw_ICI           0.956 ± 0.036  0.379 ± 0.103  0.540 ± 0.036\n",
       "Supervised_Dual   0.675 ± 0.058  0.270 ± 0.095  0.611 ± 0.080\n",
       "Temporal_Mel      0.069 ± 0.012  0.130 ± 0.074  0.569 ± 0.094\n",
       "WhAM              0.066 ± 0.016  0.101 ± 0.075  0.667 ± 0.155"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "completion = json.loads((RESULTS / 'completion.json').read_text())\n",
    "print(json.dumps(completion, indent=2))\n",
    "assert completion['completed_runs'] == completion['expected_runs'] == 72\n",
    "summary = pd.read_csv(RESULTS / 'summary.csv')\n",
    "summary['mean ± sample SD'] = summary.apply(lambda r: f\"{r['mean']:.3f} ± {r['sd']:.3f}\", axis=1)\n",
    "for protocol in ['random', 'date_grouped']:\n",
    "    print(protocol)\n",
    "    display(summary[summary.protocol == protocol].pivot(index='model', columns='task', values='mean ± sample SD'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "557f6ede",
   "metadata": {},
   "source": [
    "## Comparison\n",
    "\n",
    "Primary macro-F1 uses true test classes seen in training. Candidate/support sets differ between ordinary and grouped identity tasks, so compare models within each protocol and consult support counts. Error bars are sample SD, not 95% confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8debeda7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-20T16:41:46.176576Z",
     "iopub.status.busy": "2026-09-20T16:41:46.176463Z",
     "iopub.status.idle": "2026-09-20T16:41:46.185230Z",
     "shell.execute_reply": "2026-09-20T16:41:46.184713Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protocol</th>\n",
       "      <th>seed</th>\n",
       "      <th>train</th>\n",
       "      <th>validation</th>\n",
       "      <th>test</th>\n",
       "      <th>id_train_classes</th>\n",
       "      <th>id_test_eligible</th>\n",
       "      <th>id_test_supported</th>\n",
       "      <th>id_test_supported_classes</th>\n",
       "      <th>unseen_ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>0</td>\n",
       "      <td>828</td>\n",
       "      <td>227</td>\n",
       "      <td>328</td>\n",
       "      <td>9</td>\n",
       "      <td>213</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "      <td>5719, 5720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>1</td>\n",
       "      <td>901</td>\n",
       "      <td>211</td>\n",
       "      <td>271</td>\n",
       "      <td>7</td>\n",
       "      <td>121</td>\n",
       "      <td>111</td>\n",
       "      <td>6</td>\n",
       "      <td>5973, 6069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>2</td>\n",
       "      <td>883</td>\n",
       "      <td>239</td>\n",
       "      <td>261</td>\n",
       "      <td>7</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>884</td>\n",
       "      <td>222</td>\n",
       "      <td>277</td>\n",
       "      <td>11</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>884</td>\n",
       "      <td>222</td>\n",
       "      <td>277</td>\n",
       "      <td>11</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>884</td>\n",
       "      <td>222</td>\n",
       "      <td>277</td>\n",
       "      <td>11</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       protocol  seed  train  validation  test  id_train_classes  \\\n",
       "0  date_grouped     0    828         227   328                 9   \n",
       "1  date_grouped     1    901         211   271                 7   \n",
       "2  date_grouped     2    883         239   261                 7   \n",
       "3        random     0    884         222   277                11   \n",
       "4        random     1    884         222   277                11   \n",
       "5        random     2    884         222   277                11   \n",
       "\n",
       "   id_test_eligible  id_test_supported  id_test_supported_classes  unseen_ids  \n",
       "0               213                186                          3  5719, 5720  \n",
       "1               121                111                          6  5973, 6069  \n",
       "2               145                145                          6         NaN  \n",
       "3               151                151                         11         NaN  \n",
       "4               151                151                         11         NaN  \n",
       "5               151                151                         11         NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename=str(RESULTS / 'figures' / 'comparison.png')))\n",
    "display(pd.read_csv(RESULTS / 'split_support.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c562b141",
   "metadata": {},
   "source": [
    "## Paired comparisons and identity strata\n",
    "\n",
    "The per-unit/type summaries subset the global classifier’s predictions; they do not refit within-unit classifiers. Very small or single-identity strata do not support broad conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d5f3997",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-20T16:41:46.186309Z",
     "iopub.status.busy": "2026-09-20T16:41:46.186188Z",
     "iopub.status.idle": "2026-09-20T16:41:46.203331Z",
     "shell.execute_reply": "2026-09-20T16:41:46.202870Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protocol</th>\n",
       "      <th>model_a</th>\n",
       "      <th>model_b</th>\n",
       "      <th>task</th>\n",
       "      <th>paired_runs</th>\n",
       "      <th>mean_delta</th>\n",
       "      <th>sd_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.017480</td>\n",
       "      <td>0.006837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.077994</td>\n",
       "      <td>0.038518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.038928</td>\n",
       "      <td>0.043129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_swap_ID</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.010135</td>\n",
       "      <td>0.025492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_swap_ID</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.085534</td>\n",
       "      <td>0.074021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_swap_ID</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>0.052794</td>\n",
       "      <td>0.095301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>Supervised_Dual</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>0.003534</td>\n",
       "      <td>0.014468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>Supervised_Dual</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.039788</td>\n",
       "      <td>0.081399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>Supervised_Dual</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.095826</td>\n",
       "      <td>0.039724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>ICI_Temporal_Mel</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>0.051940</td>\n",
       "      <td>0.010066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>ICI_Temporal_Mel</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.082260</td>\n",
       "      <td>0.064305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>ICI_Temporal_Mel</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.135778</td>\n",
       "      <td>0.073042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>AVES_ICI_Adapter</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.050629</td>\n",
       "      <td>0.007266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>AVES_ICI_Adapter</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>0.270426</td>\n",
       "      <td>0.067840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>random</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>AVES_ICI_Adapter</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>0.005932</td>\n",
       "      <td>0.015594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.112680</td>\n",
       "      <td>0.090799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.113409</td>\n",
       "      <td>0.048259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.023920</td>\n",
       "      <td>0.057532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_swap_ID</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>0.037218</td>\n",
       "      <td>0.064539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_swap_ID</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.026341</td>\n",
       "      <td>0.089267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_swap_ID</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.037998</td>\n",
       "      <td>0.119123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>Supervised_Dual</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.037581</td>\n",
       "      <td>0.145719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>Supervised_Dual</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.173084</td>\n",
       "      <td>0.075422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>Supervised_Dual</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.012491</td>\n",
       "      <td>0.074419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>ICI_Temporal_Mel</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.033691</td>\n",
       "      <td>0.076842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>ICI_Temporal_Mel</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.165256</td>\n",
       "      <td>0.066301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>ICI_Temporal_Mel</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>0.001915</td>\n",
       "      <td>0.017167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>AVES_ICI_Adapter</td>\n",
       "      <td>unit</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.094710</td>\n",
       "      <td>0.059657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>AVES_ICI_Adapter</td>\n",
       "      <td>coda_type</td>\n",
       "      <td>3</td>\n",
       "      <td>0.319098</td>\n",
       "      <td>0.054103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>date_grouped</td>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>AVES_ICI_Adapter</td>\n",
       "      <td>individual_id</td>\n",
       "      <td>3</td>\n",
       "      <td>0.176067</td>\n",
       "      <td>0.025556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        protocol       model_a           model_b           task  paired_runs  \\\n",
       "0         random       DCCE_ID        DCCE_no_ID           unit            3   \n",
       "1         random       DCCE_ID        DCCE_no_ID      coda_type            3   \n",
       "2         random       DCCE_ID        DCCE_no_ID  individual_id            3   \n",
       "3         random  DCCE_swap_ID           DCCE_ID           unit            3   \n",
       "4         random  DCCE_swap_ID           DCCE_ID      coda_type            3   \n",
       "5         random  DCCE_swap_ID           DCCE_ID  individual_id            3   \n",
       "6         random       DCCE_ID   Supervised_Dual           unit            3   \n",
       "7         random       DCCE_ID   Supervised_Dual      coda_type            3   \n",
       "8         random       DCCE_ID   Supervised_Dual  individual_id            3   \n",
       "9         random       DCCE_ID  ICI_Temporal_Mel           unit            3   \n",
       "10        random       DCCE_ID  ICI_Temporal_Mel      coda_type            3   \n",
       "11        random       DCCE_ID  ICI_Temporal_Mel  individual_id            3   \n",
       "12        random       DCCE_ID  AVES_ICI_Adapter           unit            3   \n",
       "13        random       DCCE_ID  AVES_ICI_Adapter      coda_type            3   \n",
       "14        random       DCCE_ID  AVES_ICI_Adapter  individual_id            3   \n",
       "15  date_grouped       DCCE_ID        DCCE_no_ID           unit            3   \n",
       "16  date_grouped       DCCE_ID        DCCE_no_ID      coda_type            3   \n",
       "17  date_grouped       DCCE_ID        DCCE_no_ID  individual_id            3   \n",
       "18  date_grouped  DCCE_swap_ID           DCCE_ID           unit            3   \n",
       "19  date_grouped  DCCE_swap_ID           DCCE_ID      coda_type            3   \n",
       "20  date_grouped  DCCE_swap_ID           DCCE_ID  individual_id            3   \n",
       "21  date_grouped       DCCE_ID   Supervised_Dual           unit            3   \n",
       "22  date_grouped       DCCE_ID   Supervised_Dual      coda_type            3   \n",
       "23  date_grouped       DCCE_ID   Supervised_Dual  individual_id            3   \n",
       "24  date_grouped       DCCE_ID  ICI_Temporal_Mel           unit            3   \n",
       "25  date_grouped       DCCE_ID  ICI_Temporal_Mel      coda_type            3   \n",
       "26  date_grouped       DCCE_ID  ICI_Temporal_Mel  individual_id            3   \n",
       "27  date_grouped       DCCE_ID  AVES_ICI_Adapter           unit            3   \n",
       "28  date_grouped       DCCE_ID  AVES_ICI_Adapter      coda_type            3   \n",
       "29  date_grouped       DCCE_ID  AVES_ICI_Adapter  individual_id            3   \n",
       "\n",
       "    mean_delta  sd_delta  \n",
       "0    -0.017480  0.006837  \n",
       "1    -0.077994  0.038518  \n",
       "2    -0.038928  0.043129  \n",
       "3    -0.010135  0.025492  \n",
       "4    -0.085534  0.074021  \n",
       "5     0.052794  0.095301  \n",
       "6     0.003534  0.014468  \n",
       "7    -0.039788  0.081399  \n",
       "8    -0.095826  0.039724  \n",
       "9     0.051940  0.010066  \n",
       "10   -0.082260  0.064305  \n",
       "11   -0.135778  0.073042  \n",
       "12   -0.050629  0.007266  \n",
       "13    0.270426  0.067840  \n",
       "14    0.005932  0.015594  \n",
       "15   -0.112680  0.090799  \n",
       "16   -0.113409  0.048259  \n",
       "17   -0.023920  0.057532  \n",
       "18    0.037218  0.064539  \n",
       "19   -0.026341  0.089267  \n",
       "20   -0.037998  0.119123  \n",
       "21   -0.037581  0.145719  \n",
       "22   -0.173084  0.075422  \n",
       "23   -0.012491  0.074419  \n",
       "24   -0.033691  0.076842  \n",
       "25   -0.165256  0.066301  \n",
       "26    0.001915  0.017167  \n",
       "27   -0.094710  0.059657  \n",
       "28    0.319098  0.054103  \n",
       "29    0.176067  0.025556  "
      ]
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>protocol</th>\n",
       "      <th>seed</th>\n",
       "      <th>grouping</th>\n",
       "      <th>stratum</th>\n",
       "      <th>n</th>\n",
       "      <th>n_true_id_classes</th>\n",
       "      <th>macro_f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>caution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>date_grouped</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "      <td>0.197058</td>\n",
       "      <td>0.155914</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>date_grouped</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "      <td>0.215309</td>\n",
       "      <td>0.209677</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>date_grouped</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>111</td>\n",
       "      <td>6</td>\n",
       "      <td>0.322351</td>\n",
       "      <td>0.297297</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>date_grouped</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>111</td>\n",
       "      <td>6</td>\n",
       "      <td>0.406426</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>date_grouped</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "      <td>0.252342</td>\n",
       "      <td>0.365517</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>date_grouped</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>145</td>\n",
       "      <td>6</td>\n",
       "      <td>0.221774</td>\n",
       "      <td>0.268966</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>0.574243</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>0.664860</td>\n",
       "      <td>0.736111</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>0.629429</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>0.628698</td>\n",
       "      <td>0.659722</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>DCCE_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>0.440120</td>\n",
       "      <td>0.590278</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>DCCE_no_ID</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>unit</td>\n",
       "      <td>F</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>0.562493</td>\n",
       "      <td>0.638889</td>\n",
       "      <td>Subset of global predictions; no within-unit r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model      protocol  seed grouping stratum    n  n_true_id_classes  \\\n",
       "60       DCCE_ID  date_grouped     0     unit       F  186                  3   \n",
       "75    DCCE_no_ID  date_grouped     0     unit       F  186                  3   \n",
       "244      DCCE_ID  date_grouped     1     unit       F  111                  6   \n",
       "260   DCCE_no_ID  date_grouped     1     unit       F  111                  6   \n",
       "436      DCCE_ID  date_grouped     2     unit       F  145                  6   \n",
       "452   DCCE_no_ID  date_grouped     2     unit       F  145                  6   \n",
       "680      DCCE_ID        random     0     unit       A    5                  2   \n",
       "681      DCCE_ID        random     0     unit       D    2                  2   \n",
       "682      DCCE_ID        random     0     unit       F  144                  7   \n",
       "709   DCCE_no_ID        random     0     unit       A    5                  2   \n",
       "710   DCCE_no_ID        random     0     unit       D    2                  2   \n",
       "711   DCCE_no_ID        random     0     unit       F  144                  7   \n",
       "1016     DCCE_ID        random     1     unit       A    5                  2   \n",
       "1017     DCCE_ID        random     1     unit       D    2                  2   \n",
       "1018     DCCE_ID        random     1     unit       F  144                  7   \n",
       "1042  DCCE_no_ID        random     1     unit       A    5                  2   \n",
       "1043  DCCE_no_ID        random     1     unit       D    2                  2   \n",
       "1044  DCCE_no_ID        random     1     unit       F  144                  7   \n",
       "1336     DCCE_ID        random     2     unit       A    5                  2   \n",
       "1337     DCCE_ID        random     2     unit       D    2                  2   \n",
       "1338     DCCE_ID        random     2     unit       F  144                  7   \n",
       "1364  DCCE_no_ID        random     2     unit       A    5                  2   \n",
       "1365  DCCE_no_ID        random     2     unit       D    2                  2   \n",
       "1366  DCCE_no_ID        random     2     unit       F  144                  7   \n",
       "\n",
       "      macro_f1  accuracy                                            caution  \n",
       "60    0.197058  0.155914  Subset of global predictions; no within-unit r...  \n",
       "75    0.215309  0.209677  Subset of global predictions; no within-unit r...  \n",
       "244   0.322351  0.297297  Subset of global predictions; no within-unit r...  \n",
       "260   0.406426  0.405405  Subset of global predictions; no within-unit r...  \n",
       "436   0.252342  0.365517  Subset of global predictions; no within-unit r...  \n",
       "452   0.221774  0.268966  Subset of global predictions; no within-unit r...  \n",
       "680   1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "681   1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "682   0.574243  0.687500  Subset of global predictions; no within-unit r...  \n",
       "709   1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "710   0.333333  0.500000  Subset of global predictions; no within-unit r...  \n",
       "711   0.664860  0.736111  Subset of global predictions; no within-unit r...  \n",
       "1016  0.761905  0.800000  Subset of global predictions; no within-unit r...  \n",
       "1017  0.333333  0.500000  Subset of global predictions; no within-unit r...  \n",
       "1018  0.629429  0.666667  Subset of global predictions; no within-unit r...  \n",
       "1042  1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "1043  1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "1044  0.628698  0.659722  Subset of global predictions; no within-unit r...  \n",
       "1336  1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "1337  1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "1338  0.440120  0.590278  Subset of global predictions; no within-unit r...  \n",
       "1364  1.000000  1.000000  Subset of global predictions; no within-unit r...  \n",
       "1365  0.500000  0.500000  Subset of global predictions; no within-unit r...  \n",
       "1366  0.562493  0.638889  Subset of global predictions; no within-unit r...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.read_csv(RESULTS / 'paired_differences.csv'))\n",
    "strata = pd.read_csv(RESULTS / 'identity_strata.csv')\n",
    "display(strata[(strata.grouping == 'unit') & strata.model.isin(['DCCE_no_ID', 'DCCE_ID'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133d1ee5",
   "metadata": {},
   "source": [
    "## Corrected recording-year diagnostic\n",
    "\n",
    "All dates are retained. Layer profiles use validation scores and are descriptive across correlated layers, not causal evidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c5e30ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-20T16:41:46.204590Z",
     "iopub.status.busy": "2026-09-20T16:41:46.204471Z",
     "iopub.status.idle": "2026-09-20T16:41:46.208373Z",
     "shell.execute_reply": "2026-09-20T16:41:46.207841Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BdrQetrdBgwZqxLc2Oh+j57GP9JCJyFHOPj7Rn8Pxic8H8+N7izY+vsfIfoXsYshCi/fBfYw8JyIiIufC+WO0dwH/a4cMGaKy8+BcLM4Zoh3zxhtvqP/T9rRj0NZEuxftamQzR3sc/9v37dun2kxonyIzCDKmIzuq3ocffqjap8jAr2UZNdeGMj2PiXPJyJCpZXLH+uP8Is69o82I/gHOIaNdgXYRts1SliV7YH+9+uqrxr/Rxsb7oU+B87ZoE/3+++8OLRPnfHGOFduN9UcbCeeC0QZE+8rZ530zC44VtE+x79F/QpZ8tF3xGPpMaMePGDFCtd2R/cecsWPHquyugPY02oJoy6Ovde3aNbWsTZs2qX2NbURfR99vzIw2ui24joOsR1o/Bv1ba9cZtIynaKOj7W76PdKyLeHzxT5EpQa8Dp8x2vjoF+C4xzbhPDq2KbPkhN8IW/B9wr7UKnNgf+L4RSYj9Fnw+Lx589Txiwxm2A5keLUG1zxwPQnHPzJJob+DaxjIEjZjxgzj9TRsI5ZN5NayLPSJiHKcgQMHGiNxTTOSdOzY0fjc8ePHrS4nt2RICgkJMSxbtkxNGGWLSP6RI0emGo2AkdGWsvYkJyeriHv9SOlnn31WRekvWLDA8OmnnxpatWqVKjofUdm2YB5tvZYuXaoi6F955RVDqVKlUkVah4WFZWj7u3XrZlwejg9LkdvYTqxLejIkaaMnEGn+xRdfWNxe/Qj01atXp5kH6/bvv/9a3Z6NGzcaR39jBK8lpiM4VqxYkWaemzdvpsoitHPnTkN6LVy4MNVoYGuj5zF64/Dhw6kewyjp4sWLG5cxderUNK9LSEgwjBgxwjgPMnKZgyxU+pHzGP1vCp+TaWS/OcHBwYZDhw5Z3fb//e9/akQFloGR4eZGY/j7+xtHM+A7aS3Tlq3fLiIiInIdfdsQU8mSJVV2T2t27dpluHbtmsXn7969a3j55ZdtZpLU9x+0trK5dgXa1to8NWvWNLssZD7Sj0LGqGZT27ZtS5N1yFwbb/v27caMkpj/n3/+MdvO1bKpYnr11VfNrpc2AlRrW2M9TWHEdv78+dU8aGNVqVJFTRcvXkwz7/vvv29cHvpA6YU2pbYcc/tKnwFJnwkoM9v7Gc2QhH5W0aJFjfv6hx9+SDNPRESEyqSrPwYs9Y23bNlite8WFRWVakT15s2bzc5nb0Yue/v1mXV8WsrMHBsba2jevLlxHvSTiIiIyLnQ7tD+1+Ict7mshCdOnFBtdf3/bkvZT3CeFucdLcH5wRo1aqhl+Pn5qTaSObYyseshU0mjRo3UvMios2jRIrPzXb9+3ZhBCecfjxw5YkgPnAfWrgdgOb/++qvZDO9ow9rT9jNtFyGrqCXOPO+bWRmSMLVv397sdQ1kW9X3/8zNg+xP2jyo1ID9bc63335rnK9///6Z3ka3BRlntfPWyAKEaySWzJkzx/h+qFZiCsemtb4SjvnPPvvMuAxkEs6sDEk54TfCnmNYXzWmevXqZjMuI+NYYGCgcb6VK1da3efaPvvvv//SzIfPV/sdQaY1axmeidwBA5KIKF3u3LljLImAQArTBhJKJmj/NF9//XWry8otAUloLJkGXmgT0i7iJD0CJqzB8xMmTFCNKUvLQgcKjRlrjVY9BF1YWhYaSGPHjjUbSOIInOzXlomLFDgJ7ihbAUl79+41Po+AKmtu375tbOB37tzZkF4oN6C9p7kLL6Dfn/iMLUGnxp75rEFnAg1ebTlr1651eBlffvml8fWPP/641WOxXr16xnnXrVuX6nlclEMAFp4rUqSI1dShffv2TbWfMmLw4MFqGfh9Mg3uwwVI7T2QGpWIiIhyTkDSH3/84ZTloo2jBYojiN5W/wHp+1HewRJ9KSlz7UV9WQhLgSHwzTffpNpecwEi+sEL1kqa4SStFkhUsGBBs8Er+gsbzz33nMVlYYCEfr0sBdYjOETrw1SqVMmQXr/88otdbeZJkyYZ5zN3kceZ7f2MBiShnJz2vLmgeg1K5+n7gRnpG+PEvBZshUEEWRGQlFnH5zPPPGNxWeij2DMfERERpU/v3r1tBvMDzlXbE2xgDwSNa8sxFzTvaLABBg5r83711VdW58XgTq3MGoJ20kMfCPL8889bnA/nUjHY1FbbT98uQnsrK877ZmZAEs7ZWxtEiuAhbV4EFZnS1rtcuXI2r0E89dRTal58pqZtfVe00YcPH25cDgJ5LEHAlhbQdunSpXS/X5s2bYyl2zIrICkn/EbYOoYxsKlEiRLqeS8vL6uDqn///Xfj++Jcga2ApHnz5llc1ltvvWXXfETuwMPVGZqIKHtC+nakFQSUPEIKSD2k/URaeECaTy0FKKWFfdeuXTuVphJp663B8yhT8O677xpL5JlCakqkwUfZqYx68MEHVdpSpNDPiF9//dV4H+kwtWPDmXCcaftz3LhxVudFilKUlACU00BK2/TuHw3Si9r67EaPHm3xeZQq0CDtZnogfTBKkwCOqS5duji8DJRR0yBVqq1j0dzrAGXRkIIUhg4darV0oH45GaV9Jvh9OnToUKrn9MdxevcxERERuR+k80eZA2dAGwdlhrX2nbnytnoorYCSWOlp46FMHFKsA9Lpo91tyfDhw62m0Ud7FuUXAGVtUeLN2v7SSpihZK1WLs4Sa21YlL7ToEQbSoSZg7JbWgk3bDfK7qbHo48+qtLUm/YxTGnPYV57Uv87o72fXvoSAdb6MVq5YWdAuYq6deuq+yiLltky8/hEeUBLUKICZRqB7X8iIiLn/39HaV5AuS9r7RSUHUpPyV5b7TNntGO0c8oo5YxSYNZUq1bN2N611Uax5I8//rCrHYNzqSjj5YgXX3wxS877ZiYcRyi1ZYm+vWxaauvgwYPG88Eo72zrGoR2zOLa1YYNG1zeRtd/3lr5aVOXL19WZb218/9ly5bN8HcJpdu0coXOlFN+I2zZvn27sSwiyrZp/SxzUB4RpeMBZadDQ0MtzouyhAMHDszU60lEWSWlV05E5CDUMdaYaxijsYeTxWg4oS4vGh44WZ+bPfTQQ8YLGqgbi8YGasdOmzZNnTDHhJP906dPN540NbVmzRpV8zg8PFw1OMePH68umCDQAnWdUVsa9YWx3I4dO6rAMdQ1tgb1cLX1QuMbtWdxsv2bb76RlStXqgkNJdTJTm8gkVY7F8FCzrpYZGrr1q3qFhdq7LlYoAUh4fbcuXNmG7yoI4z6xmgcoqY09rsWZGOuM2Crw4pAKEtQ71iDeuQZ2c9gqYa2NTgOdu/ere4XLVpU1Ti2Rh/wZNq415YDOBat0Wp4R0ZG2lxHvA9+V/777z/1ueE1qAtt6TPBxT0N6rDjpAGOj/Xr16vfKHTUW7durWpNExERUfaEwH7TARKWJCcny/Lly2XJkiWyf/9+uXr1qmpP4HFTeDwiIkK1ISx54IEHrL6ftTaeI+0lDEbAdqJtbg5OvmvtW/QTfHx8bLbj5syZY2xfPf7442bnQ/u/du3aFpeDE8saS8FIpvOizXnnzh0pWbKkOCp//vyqDYcLRwjExz5EW1IPbb1Tp06p+3369FGvscSZ7f30wL7AoALAhZc6depYnR/HyaxZs2wuF8fCokWL1AUvHBs4QY7gHnMBdpmxXVl1fOKztXbCH++Dfs3169fT3cciIiIiy//ftXYTAuttDbRFO+b48eM2dyfOmc+bN08F/OAiO/6Hx8TEmJ3XGe0Y7ZwyAktw7t0WbTsvXLigBkQ6MpAXfQ4MJga0hXFe3hq0m7766iu7lo31atGiRZac981MtvpFGOSA4DH00/T9Kf1nCWh7ot9nzZUrV4z39cdmZrXRbcE5agTn49jCdZ6vv/46Tbv5f//7n7HvaisQCue/cX0I+wnXjtC/tZQ4APsCx4Uz5ZTfCFv016LsGSDeuXNnOXPmjPG7Zel6GY51a/vMGdeTiLIKA5KIyGH4J6/9k8UJ+KpVq5qdb/DgwcZI7p9++inXBySl+vH18pLSpUurE6sI9nn66adVIwpBQOjEfPrpp2n2JzpEiBRHgxOvWbhwoXh43E90h8hqBCih4YaRoBh1jGAxnIy392Q/GjhoZCN4DNM777wjH374oSxevFhd5MFJ7fTQGn5YtrVMORkRHBxsbHzhIoUjTBts6HS8+eab8vnnn5u9OGUOOkHW2GrQ6zNepXfEuL6BnZ4RBdgGrfFu6Xuth88TF+hw4QaBh3q4uKfRov4twbGFzAJahgBz0HlBZgBt1FJ6P5MZM2ao7wieQ6cUEy60IbAPF/k6deqkRvrrv1tERETk3vQn4my1lZAtRzu5bA9bAUkZaeM50l6yNY++LYZAeFv085i24/TQdrcW7KXfPmS+yez2LqCPo7UJ0d80DUjSjya2NKo8M9r76YF2dHR0tFOOAc3hw4dVxmIEWLlqu7Lq+MQxZysYUTvuMnLMERERUVrObMtqcL77ueeeU22krGjHIGAbg3MBwe7pOafsSEAStks791qpUiWb89szj75dhKykWXHeNzPZcx4Z+wXnkW/fvq0Cj7T2nnZ9ACZNmpTu6wOZ0Ua3B7btySeflMmTJ6v1QQUC02NS6+vguEOb3xysP647OZLFKzP6BDnhN8KVfZ2suJ5ElFUYkEREDkNwkcZa2lBc9MfFAURXr1ixQqV9dHaUdU6AwAcESWD0KhpSGPWAwCLTbDpIDYqT5Zj/yy+/tBgwgZHJKJH13Xffqah3ZDbCyfb0QMMdQUgIakJUPoLRatWq5fBytIafVl4hM9jbCDXHdBQ0Gv1aUBiCtBCkglSf5cuXV8ErWjadI0eOqKAtsFWWMCsCXPQN7PTsa32GInuzYeF9sO9NsxuhQ6+xNird3vd74YUXjBee0NhGyT1cfMJvDF6rjRbYuHGjGj1i6TPByAJ0WCdOnKiOaYxkQgcTr8P0/vvvS1BQkArEQweQiIiI3J89FwGQUbFr167GVObolyCjJEa7InMPTt5r7TW0xzdt2pTpbTxntpccbcfp24rWslQ6sn1ZFdCNfiYGd+AEN0b8IuOs1hZEJlqcpAa0E/Vp7DO7vZ8ezm4z46IMtkVL/V+uXDk10ASj75HyH8e5FsCDUtrIEGVvQFZGuMPxSURERO7djtmyZYsqT6S1TZDBB+0aDGJEYIz+4rsWpJHR9llGzieDpcyalmhBLs7aZ470iZx53jczObpfcBxqx4azrg84+9h2BK63oa+iBR/pA5JQjg6DDwBVMZApyhwMZkd2JEBVAmTgadCggcoChu3R2tDoS2l9p+zQ13HFb4Q92Nchso0BSUTkEJzg1WcoQZAAJnsuAKAkmbW6yLkZGvfIzoKod0T1oxwV6s1qzp8/b7x4goAgnIC3Bg0xBCSBPeXLLEHjFCkktZIHqE+cnoAkNI5xclzfCM2MfYjSD7iIgLSm6YUAlY8//tjYYMfFKH3ZLz13K/Ol74SkZ19je811kK3R3kf/WtOLCJZSpupZez+MbtFKNqAuNo5DSyOE9Kl2LalYsaIqzYHvyI4dO1SdZ5S7w3Lx+eP9kPIWx9GECRNsLo+IiIjc34IFC4ztabRvly1bZvGEJ/otWcGZ7SVH23H6tqJpO87doY+CE9HIboTAG4z81fpOuK8F42AecwEr7tTed+YxAMi4q23/kCFDZPbs2RbLgaPUd1bJTccnERFRbuHsdgwGD2qBBt9//72MGDEiXctJ7zag4gDODWYmff/DGfvMVed9HeVIALyj+0X/GervY+Bp+/btHVpPc8vJ6s8JAwkwoHbPnj3qWhGudxQuXNjuTLAI2tGCkerXry/r1q1TAxPMQdnqzJQTfiPswb4OkW0cSkREDlm9erWEhIRkOLMSWW+4oKFpKb2lpch3PX1ZiYw2wKytl70QRAI4OY7ApMws1YH3QABceiFARdtnSP9p6eIEZCTwKTNo+xnsqbdsCseWNlpBq2NszY0bN4wjT0yD5PR/21oWSmacO3fO4vPoQGIeQLYva+mKHflMMEIcHdO33npL/bbh2JkyZYpx1DiyJWlpm4mIiCh7007KwvTp062OvsyqNp4j7SVb82C0q8aeUl36eWwNdnBHCB7X6E/M6+/r53HX9j76bdqxmNFjQH+cIwjpiy++sBiMlNV9mdx2fBIREeUGzmzLIjvN1q1b1X0EY1gKNHB2GwZtMS1oAuWdMxveTzv3au1cqMaeeVxx3he0bDS2skThnK4j1wMcOY+M8tL6rDj6Ut4Z+Tyd3UZ3lNaPwcB1ZPgHBOJgkI1WTq9Lly42+70YgGApGCkr+gM54TfCHuzrENnGgCQiSndQEUZcvvfeezYnrSbxwYMHZf/+/dzjdjS4TEvb6YOQLl26ZHMf6htdqB+dWetlr9atWxs7DCjflxnatm1rrJeLkQDppQ+4Q7pPa9auXSvuRNvP8Oeffzr8egTioAya1ulEaTNr9HWoUSpQT/83Aoqs2b17t9V6zln1meAExOuvv26sv41OH9aNiIiIsj972xMIULbVBnIWR9pLaJcgo6MlGP2qnYzfvHmzzQB9a+247ADbW7duXXUfpa8RYIQR3LgP9erVU5O7t/fR/sYJde3YQwk1azZs2GD1eW3b0AfURlKbg3452vvW6LNLaYMD0iu3HZ9ERES5Af6/+/j4qPvILGSrNJK1dgwGBKIyg7PaZ460Y5AZCRDk4uzAEnPrhTJTcP36dTlx4oTV+dFuchZnnvcFra158+ZNq207lEB2ZMC0rX7R3r17jeeRte0xvT5guv6ubqM76oknnjAOLNAGXOBY0IKsBgwYYHHggb19HQT4aGXKM0tO+Y2wRf/9QEYqW/TzsK9DuQUDkojIbmiorly50hggM2vWLJUm0dakL+nGLEnm7du3TzWmtbIApo3pKlWqqGwuWkASSkxZg/q/Gq3xnB5o5K5atcr494MPPpiu5ehHJyMyPzPSZg4ePNh4f9KkSemuD6yvZ3z27Fmrn5n2fXAXGN2NtK5aJyU9HS8tGAc+++wzi/Nh/6JMhrnXQffu3Y0djrlz50pYWJjFZU2dOtUpnwkuQKGWdkYFBQUZ72sdHSIiIsre7G1PoJRXRrJtOtrmaNCggbqPlPzaiE9zfvzxR6vZShHsgfaXdlEA7S9L0J/QRtciILtr166SHWllCtC3WLp0qZq0UgCWShi4Y3v/0UcfNd6fNm2axflwccFWOUFt23DhJDIy0uJ8yATqSImFjPbfcuPxSURElNPh//vDDz9sbKf873//szgvMpNby+Zub/sM7RtkO3VmOwaDrjXvvvuuZLbevXsb73/55ZcW50NWIX32T2dw1nlfqFWrlrpF38laP+arr75yaB2xzdYC5/Xt5T59+qR6DtdBateure4vXLjQZiBRVrXRHaXPgIR9e/HiRbvKtTnyXcK1PbTLM1NO+Y2wBdfMSpYsqe6jzJ5WKt4c9Fm1bLCtWrVSnzVRbsCAJCKyGxpW2sl5NPby5cvncEQ3Gh220njmFBihO2HCBJsjT3GiGw1cLRJ74MCBaUazYl/rOyvoKKEhas7kyZON0eRo9D3++ONp5nnjjTdspq5Ew65Hjx7Gk/oYYaA16B31wAMPGBufaHA98sgjFlO1Yj+kJ7tP8+bNjZ0jNNSffPJJq1l3EGiCBuCMGTNSPa4P4Jo9e7acP38+zWuxDXgvR+pfZwWM3tBfXMBoCWsjaTDqx7RjNnToUGNDGN9Xc51GdEpHjx5tHEmDALqOHTummgfpYLUgMXRu8HnExsamWdY333wjixYtsrpd+gA9dIbNBTft3LlTnnnmGavLwUjwDz74wGrZSayrlgoX+9PSyHoiIiLKXvTtiXfeecdsO+777793+IR5Rr366qvG+2g7mWujo8QY2u+2vPbaa8aRnuPGjZN///03zTxoR/Xt29d40vX5559PVe45O0G/SdtenKDXTtLjMTxnibu199G30zLRYgCPuWAd9C3RtrfWv9Ef5+hTvf3222mex+M4/pcvX25zvSpWrJiqz5pRue34JCIiyg3wP13z8ssvm826g3bVsGHDrC4H/++1KgsI1F+2bJnZ9lC/fv3sqh7gSDsGbQ+tDYWg6DFjxli9foHzm2iv6QcEO+Lpp582nvtH/8NckAbeA+dSHSl1Zg9nnfeFhx56yHgf7UtkdDWFtjYmR2AQBq4nmQsS+fbbb42B6wgAMW3z41wuBpgArmPheoSt7PcIHhk5cmSmttEzMsAb7fc5c+bIkiVL1N8YjGxtALq+34vrBOY+F1SwePPNNyUr5ITfCFswKHvs2LHGa05Yh2vXrqWZDwOpUTJck1WfAZE7sFxMnojIhD67kbUobEsR3ci0g9SKCDZBQ98VcLIZDTg9fUYVpAQ1zYiCE9ENGzZMsyw0QtGB0IJ1TAM/sBw0gDHaAKlfETCDTEfILoWGIAKK8BrU9dVOdKNBqR99YBpohHSO6IggkKROnTqqYYpgHwQsYXkIpEBghgYl88qWLZtmWd99951aL7wWEdzVq1dXHSGs89WrV1U5CHxeWucLDXxHOw+mfv75Z5WCEp8BthmpNdFgRyMZ+wQnn/FZYBRycHBwulJlYuT4qVOn5PDhw2oUBNJzIiALjfSAgADVmbty5YoKTMG+xHuaNnZRaxoBdwhWQgcIqUXRUERgCj4nZKeaN2+eKg2Hi0a4707Q4EWnEYE+2L727durDiJGFqNuMz5TBJshJSsCt/B90Aea+fn5qe96r169VAcUHQU08PGdRZARjrP58+cbvzeY39I+mDJliqxZs0Zl2sIoBxyzCBrC9wDrhn2MzwEdAHQqLKUKbtGihcr+hCxiODbwPcHFCRy3+EzxvcXnDeisWxqVgrrnGOmEDFotW7ZUx361atXUNuB7heMGnXGts49llS9fPsOfCREREbke2u1oT+PENto2KJWAPg3ayghWRrsEaeTR7kUpMHtSrTsD2htof6DtjXYO2pxon6L9ipPoKEWMthYCOZBhBiMuLUHbHoFL6INgZCj6KDiZ36FDB9VfQLkEtOm14Gy8lz2ZctwV2u1o62Iwhj69P7YX7V5rr3On9j76YRgk0b9/f9UHwrG6ePFi1R5HG/nkyZOqzY52OAaymDv5rhk1apTqE6EdjwtMaF9jW3Fc48Q8jjX0hTCiHceElqXXUjloZO/FcYi+oxasr5VeCwwMdKjEQG47PomIiHIDZPhA+2PmzJnqXB/+3yOQA4+j/bpr1y7VjkEbHANUrQVFv/jii/LSSy+p+zgPiXYyloPzdmgn4Fw8zlvb0z5DAI0WbIO2NYIFKlSoIJ6enuoxnJvEBFhPBHvg/CPOGyNrEQZP4hwr2olojyHQAW0xBEKg3YntwaDH9EBpXWRwQZsPbU9sJ85roq2P90JwBs7NooQc1kEbOKkvMZVezjzvi88T+xDXKdB+xjl+7Gu0w1GODp81ztmiTYlz0fjs7KEdJziPjOUhCAVtdqynVooL7VJc39BnudH07NlTnf9FOxLbg2syuDbVqVMn1ffDa3GNCoN0cW0GAUk4LpAxKLPa6OmB/YD9j3bzJ598YrxOo69EYQ7WBf0dHMv4/qHdj/1YqVIltR/R70RAUoECBdR1Ly3QKbPkhN8IewOvsF9xXQ3HFK634DoIzjvgmhsGY+D6mBYgNmLECGMGWaJcwUBEZIe9e/ciOkRNZcuWNSQlJTm03xYsWGB8/cMPP5zquSFDhhifO3/+fJrX4jHtecybEZs2bTIuy97pp59+MrssPK7N07Zt2zTPh4WFOfQ+jz32mCEkJMTm51C1alWby/Ly8jJ8+OGHFpfj7+9v93q1a9fOcObMGYMzXL9+Xe0rW++ZJ08eq5/de++9Z/E9wsPDDf3797d7+9555500y7h586ahbt26Fl/j4eFh+OCDD+xaJ2vHSEbmteX99983+Pj42Nz+n3/+2ezrly9fbvM4KV++vDomrTl58qQhKCjI4jLwe3LgwIFUx4U5p0+fNpQrV87icnx9fQ1z5sxJ9b00/e5u3rzZ7uMCx1BMTEwGPgEiIiLKTPa2DU3bN2gzWPr/X6ZMGcOuXbts9k+stTfSM29UVJShc+fOVts5v/zyi9pO7TFsvyVo33p6elpt66DthTavJRUqVFDz4dZZn4Ot/Zoec+fOtbt9m1ntfVv7yt7jZcaMGaofZ62/eOrUKZt945kzZ6r1t7ScmjVrqra1rfY3jB8/3uoxlJ7PNyuPT0fnJSIiIsclJiYannrqKavtqk8//dRmmyg5Odnw5JNPWm0j9O7dW52vs9Qe0a9Tq1atLC7HXLvu6tWrho4dO9p13hBtmR9++CFDh8tnn31mtU3Ut29fw7Fjx4x/v/TSS05r6zjrvO+ePXsMAQEBFpfRvHlzQ2hoqM111LcjcS750UcftbhMnO+2Z99jnkKFCtn1eVrbd85qo6fH0KFD01wzCQ4Otvm6HTt2WP1cChcubPjrr79s9i/tuS5nTx8gJ/xG2PM9i4yMNPTo0cPq+uEzfPHFFy1eX3XkWqgzr5sSZTaWbCMih7MjYRSjoxH5KDeGLDiASHZ7I+LdmVbKDDCCwBSi6BEN/cUXX6hMQBhpjccQZe3r66tGqWJUKEaJHjx4UEXY26oZi4hqjFBANDWi3YOCglT9XJTEw+gKRJgj1SMi9N966y2Ly8FIWaQ4RbQ4sj/htVgGRrtiHTAiBNHmiOhGJh1kM3KGEiVKqJEHiMTHiHRE5iMaHyNv8b7YHxMnTlTrn144zpAyF6k2kWJXv30YNYFRFYi2R+1njM4wN+oW8//3339q9C5ej32MCfsBoyEw6sNcCQR3glS5yBY1fvx4lV0II5hx7GHEAI7F4cOHq8/B0qgKfGe1/YORJNgn2ueEUcwYLXTixAl1TFqDDEQYofDRRx9JgwYN1GeAdcAoF6wjRmhjtJEtGJGAebE9NWvWVN8hLAtZkpARCqO7bZVsw/GFLEjIQoYRIMi0hGXg9wy3GDGCZSA7Ao4he8tSEhERUfaA9g3aiChVUK5cOdW2QRsHbSW0edAm16e4zypoD6OPhFGcyPiDdhvaOmgrY+QkRmJj9Ke9sC3oM2AEKdo3aHuhnY8RwdooWLTJse3ZHbYH7XQN7iMjkC3u2N7HyGH98YnU/+g/YTQ3Mhuhv4hj1haUnMAIXIyoR59Ta8MjOyj6QDie7B3ti6xiKIuBjKtYFtYpo3LT8UlERJQb4Hwj2rHIDoKMHzhPjv/tyDqO6wg4v4zSrbYgcw1K8KLdgzYxzqOj7YE2Qo8ePVQWIWRPsed8HdYJGU+RWQbnuZE5X8t8YkmpUqVUVn+0Q5A9ExlOtHP5ON+Mdguy5SCTDjJP4txqRks3IzuMlrVVa/t17txZnZdEZiR9KTD0EZzFWed90Y/CuVa069C+RB8G+xr7HNmFkJ3f3HUTa7AfkMkUxwKy2GCf4DEcTzhvi/PD9ux7zINy2FOnTlUZkpC5CcclJrRrUdECxyUyXiEbVWa30dPDtEoJsgEhi48tuEaEvi3OmaN/g3VGViecj9euRaGcXVbJKb8RtuD6ArYRFSNQThCfFb4T6O/jGsmzzz6rSggiM5MzMp4RZScqBYWrV4KIKDtCYwmdAzSEcFLXVgOdiIiIiIiIiIiIiIjIlq+//tpYogpBOhignBMh2AcDsOH8+fNqEDYREeUcDMEjIkoHxHKiBjI8/vjjDEYiIiIiIiIiIiIiIqIMS0hIkO+++07dRwaeli1bcq8SEVG2xIAkIqJ0QIr50NBQVQbsww8/5D4kIiIiIiIiIiIiIiKroqKiZO/evRafv3v3ripPdvToUfU3MiOhnBoREVF25OXqFSAiyo5QWxiGDRum6iMTERERERERERERERFZc+fOHWnSpInUq1dPOnfuLLVq1RJ/f38VqISB0AsXLpQrV66oeQMCAmTq1KncoURElG0xIImIKB1eeeUVNRERERERERERERERETkCwUeYLClfvrz8+eefUrZsWe5YIiLKthiQRERERERERERERERERESUyUqVKiVLly6VVatWyb59++TGjRty8+ZNMRgMUqRIEalfv7707NlThg4dKr6+vvw8iIgoW8tjwH84IiIiIiIiIiIiIiIiIiIiIiIiJ/BwxkKIiIiIiIiIiIiIiIiIiIiIiIiAJduIiHKAxYsXy5EjR9R9pHINCgpy9SoREREREREREREREREREVEuxYAkIqJs7t9//5UnnnhCEhMT5bnnnmMwEhERERERERERERERERERuVQeg8FgcO0qEBFRet28eVMaNmwoly9flu7du8sff/whnp6e3KFEREREREREREREREREROQyHtz3RJTVrly5Il999ZX07t1bqlatKgEBAeLj4yPFihVTwTXPPvusLFu2TOLj4/nhWIF40sGDB6tgpMaNG8vChQudHowUHBwsefLkUdPcuXOz9PPYu3ev8b0rVKigttdRw4cPNy5jypQpkhMkJCTIDz/8IB07dpRSpUpJ3rx5pWzZstKzZ091DGRFnPHZs2clf/78xn2LyZqYmBhZs2aNfPDBB9KrVy+pU6eOlCxZUn3v/fz81O9A//795ffff5ekpCS712PLli0ybNgwqV27thQqVEi8vb0lMDBQmjRpImPGjJFDhw45YWuJiIgoM/z3338yevRo1Y4tUqSI+j+O9kW5cuWkXbt28vLLL8uCBQvkxo0b/ACIMtGOHTvkmWeekcqVK6vvINrT+F5++OGHagCMs4SHh8uiRYtk5MiR0rx5c+P3HucD6tevL6NGjZLdu3ena9lo97/++uvqfALOK2h9JLzPK6+8IqtXr7b42uPHj8ucOXPk+eeflxYtWkilSpVU3wJ9leLFi0vr1q3lnXfekXPnzmVg64mIiMgR7CsQZT84/49z/LiW4+vrq9rSLVu2lOnTp0t0dHSmvz+uJ+K6g/6axebNm+3uT6A/ol1nyJcvn+oXPPnkk/L3339nu31B5HaQIYmIKCvcuXPHMHr0aEPevHkRMWFzKlasmOHLL780xMfH8wMy4+OPP1b7KSgoyHDt2rVM2Ufnz583fh4//fRTln8OdevWNb7/+vXrHXptdHS0wc/PT73W09PTcPXqVUN2h8+jYcOGVr83nTp1MoSFhWXqerRv3z7N+1qzevVqu77zmOrVq2c4fvy41eXFxMQYHn/8cZvL8vDwMLz88suGpKQkJ+8BIiIiykif4LHHHrO7bZAnTx5DVFQUdziRkyUnJxvGjh2rvmOWvn8lSpQwbNiwIcPvNWXKFLvPAwwaNEj15eyB+Z577jnV7re2TH9/f4vLaN68uV3r5e3tbZg0aVKG9wURERFZxr4CUfYTFxdnGDBggNW2dOXKlQ0HDx7M1PWYOHFimvfdtGmT1dckJCQYxowZY7Mv0L9/f0NsbGy22RdE7sbL1QFRRJQ7nDlzRmVwOXHihPGxZs2aSefOnSUoKEj8/f3l1q1bKvMKooePHDmiRkNjZHS9evXUKGm6799//1WjNDGadNWqVSrbTE709NNPqxGtgAxNyApkryVLlkhkZKS6/9BDD6lsQtnZnTt3pFu3bsbvUM2aNdVIZoz8xffr+++/l0uXLsn69eulT58+KnLfy8v5/+Znz54tmzZtkgIFCjgczV+rVi31va9Ro4Zab4wOwHbt27dPfvvtN7l9+7YajdC2bVs5ePCgxeN64MCBsnz5cnUfWcEw2gCjnzHS4OrVq7Jhwwb1vUhOTpYvv/xSjW7+9NNPnbL9RERElLFMj127dpWdO3eqv5EdBdkTW7VqpdpqyPR4/fp12b9/v2rT4P86HmOleSLnGz9+vBqhC2jbI/Mo2upRUVGqL7Vu3ToJCQlRmY23bt0qDRo0SPd7nTp1Su7evavuY6Rxp06d1PKKFi0qYWFhqv2O90S21F9++UVCQ0NVViMPD8uJ3bGePXr0kH/++Uf9Xb58eXnsscfUqGiMakZGJvSdcH4BWYWtwfkIbDsyLCFTFPrZWJcLFy6ofgUys+L367333pPExER5//33070viIiIyDz2FYiypyFDhqjKDYAsqKiAUrduXZVtFW37Xbt2qet+uEaDcwHIiuxsx44dk8mTJ6v7jly3QJZUZEvVzk8MGjRI2rRpo65baJlUUfEF24f+zNKlS61Wi3CHfUHkllwdEUVEOd/NmzcN5cuXT5UBZfv27VZfs3PnTpXpxZ4oZso8rs6QFBoaqkai4v3z589viIiIsPu1HTp0MK774sWLDdmdPlL/oYceShORf+vWrVTZk7755hunrwOyTBUuXFgtf+rUqXZnSMJvgK0MVVh//ejk559/3ux8W7ZsMc5TqFAhw/79+y1mZUJmLMzn5eVluHHjhgNbSkRERJlh+vTpxv/jyPJpLSsisrds27bNoWwpRGSfffv2GTMjIXuQuRG67733nvH72rRpU/WdTK/hw4cbunfvbti8ebPFedDOL1iwoPE9f/zxR6vLfPLJJ43zTpgwwXD37l2L8168eNHic8eOHVMjo6355ZdfjPsLfYvLly9bnZ+IiIgcx74CUfazfPlyY5sc1wAvXLiQ6nlULnj66aeN8/Tt29fp64D3eOCBB9Tye/bsaWjbtq1dGZJWrVplnA+VNnbt2pVmHlyPat26tXG+n3/+2a33BZG7YkASEWU6nHjU/sm2aNHCEB4ebvdrp02bpi5EUO4MSIJHHnnEuA5z5syx6zVo7GknjIsUKWL15HR2EBISYvDx8VHbU6BAAfW3OYcPHzZud8mSJQ2JiYlOXY9HH31ULRuBT1i2vQFJjlwY0ZZXrlw5s/O89dZbxnlee+01q8vTl4P5888/nbKORERElH5NmjQx/m9G8DARGVzex5oxY4bZeRCA1KxZM+N8K1euTPf73b592675vv76a+P7tWnTxq6S0CjRnBV69erlcL+UiIiI7Me+AlH206BBA2Mb+a+//jI7T0xMTKqEBbiG4Uxffvml8boJrgvZG5CEQd/afOiHWHLp0iXjtRlsh6WBGu6wL4jcleXcx0RETrBjxw7566+/1H0/Pz9ZsGCBSp9ur7Fjx0rLli1TPYbSXUiLiAn3Yc+ePTJ8+HCpUqWKSsmI5zZv3mx8DQIwkWb+rbfekg4dOkjp0qUlb968at6KFSvKgAEDZMWKFTbXZ+LEicb31paP9PJIDY/0ikjliBTvSMUYHByc6rVxcXHy3XffyYMPPijFihWT/Pnzq3SNn3zyiTF9vS1IS//FF1+oUnfaNgQGBkrTpk3l3XffVWXurEHpO239AWnosQ+xvDJlyqgSXyih5yjsC6SjrF69uhQsWFCVyEK5LaTLRxmOzz//XKW2TG/ZNo32edvy888/G0t7PPnkk2p9sjOUJ4uPj1f3n3jiCVWazBzsbxzfgHInWvkCZ0AJhWXLlqmyCSgPh1JpzoaSbhqsvzko36CpWrWq1eVVq1bNeN/R8nJERETkfPryzSjRmhFof2rtWrTR4fDhw6odjvZ4vnz5VJsbpaHQB7EX2lxIy442rNa+L1y4sCojPW7cuDRtfEvb+dlnn6lloDwV2v1ot6MsHVKzz5w5U/UNHN2+kydPypgxY1TpXvSp9P0hc/OjTNYLL7yg2kxYB/QfUEYb5Z9Noc+G8lfaNleoUEFGjRplsU2mQfmqtWvXqn2D0ntop6Ltjb4f2mJDhw5V5a5swXza+mv7GMt95JFHVKlf7D+sf79+/Ywl/5wJ5b1effVVqV27ttq36Behn4Vt+vbbb1W/KadAWWuUQwNsK/a9OfgsXnzxRePfWumB9EAJNHvg89Xg+2wJvl+A4+zDDz+UrGBPX4WIiIjSj30F9hUsYV/BPZ0+fVoOHDig7qPP+fDDD5udD33zESNGGP9etGiR09YBJZZxzQ8++OADVcbZHsnJycZrJ+j34BqSJeiPatdcLl68KNu2bXPLfUHk1lwdEUVEOdvjjz9ujPZ96aWXnLJMZOrRZ+35+OOPjaWZ9JM++nno0KFpnjc3ISraWgYnfdp6LP/111+3uKyAgACV8QWuXbuWapSH6YS0j4iOtgYpJIsXL251/ZFa8o8//rC4DH10OEpktWrVKs0yKlSoYHeGJKSZRPp9e/ZtekeuIn1+iRIl1DKQ/efs2bM2X1O5cmXj+1oq6ZWd6DP92Co/99lnn9mdQcheYWFhKuOS6efo7AxJR48etZkh6e2337Z7+5D2VJv3wIEDTllHIiIiSr98+fIZ/zebpi93FNri2rLQRp83b54hb968FtuiyNpqWvLW1O7duw0VK1a02qbFyMhvv/3W4jLmzp1rV9sY7VWUirJ3+5AaXr//TNvopvOjzYiSx+beG21qrRxWfHx8qrTxphPagKdPn7a4nu3atbNre4cMGWI1ayme1+ZFe3/kyJEWl+Xh4WGYPXu2wVn+++8/Q5kyZayuP0oLo/+UE6xYscK4XT169LBZQlubt1ixYpm+bijPqL2fr6+v2XmCg4ONWWGfeuopQ1bp16+fcd2YIYmIiMj52FdgX8ES9hXckz676ejRo232tfXloJ2la9euapmNGjUyVouwJ0OSvp+Da0+24NqmNv+bb77plvuCyJ15uTogiohyLsQrIHuQ5qmnnnL6e2CU5po1a8Tf319l6GncuLHK3HLw4EH1mCY2NlaNqsVI7GbNmqlR08iOhIxCGDk8f/58uX37tlrW4MGDVUYaW2bMmCGLFy9WGZaQxQcjgO/cuaOWhVHHYWFh0rdvXzWys3v37rJv3z4VGY2Rx0WKFFGjPr766iu5deuWyt700UcfWRzdiew0/fv3VyNzvb291WhrZDsqUaKEREREyKZNm1Q0NUa7Pvroo7Ju3Tpj1LYlgwYNUtHc9evXV1l3kBkJyzp06JDd+//rr7+W2bNnq/sYOY5lNmzYUO37mJgYNboZI5ixfumF0clY7tSpU9UxhexHkyZNsjg/9uXZs2fV/QYNGqgpuzty5IjxPo5xa5o0aWL2dRmBEe8YBYwR85k1AhkZjF566SXj33369DE7X+/evY3rgIxjAwcONPsZ47uMjE6A7wqOcyIiInIttMG19gnakVqWk4zavXu3TJ48Wd1/5plnpE2bNqpPgMeR7QjtDGQAQpsS7XdLmV2RTQltWIyQ7Nq1q3Tp0kVlEUVfAs+jnY/nn3/+edW3MJdZBvPi9WizYT2QQRTZYdDOxuhN9F/Q/0B7tVu3bmoUJdrR1qBvgb4CtmnYsGEqgyyyGCFjErKSmtq7d6/KwopMRciohPYhRoCifYRsUWhTY1Qmsv+gP/LTTz+pDFDYP8iMFBISojJi4rNCGxDbaW4UqLa9yJDasWNHtc3oU2Ddrl27JkePHpVff/1V7X+04bGdyPZqy9tvv63WE/0r9M2QBRf9nKVLl6rMPtgWZG/CfqhRo4ZkBPoqWHesIz439N/w2SOjFPpF6OvcvHlTzYe+HPYD+ie5pW+BLGM4JnDsou+MbKWWsrU6e93wvpb6e1o2XPTtAcfGDz/8IPv371d9cvS3kUUYfQVkXdIyBKcXfj/wHoDj29KIZyIiIko/9hXYV2BfIef2K3D+Hv1ZXN86duyYas9ntI2O/jmy6mK5jlZ00PoT6WEuk6ur9wWR23N1RBQR5VwY8atF+mKEAzLdODtDEqYaNWoYrly5YvU1W7ZsUVleLImKiko14nHz5s02MyRpI0pNR1oja5AWmY2pcePGahTvr7/+mmZ5J0+eNI7+KFy4sCEuLi7NPBcvXjQUKlRIzYP6socOHTK7bjt37jT4+/ur+cqWLatGO5vSR4djGjNmjFrf9Kpdu7ZaDt4X22IJsk5lJFPRkSNHjOscFBRksU4v6Ed4o35wRhw/ftywbNkyp0w3btxI1zrg8/Hy8lLbg0xgtr5H586dSzXyPqPWr19vXJ5p9i39sWSvyMhI4z5ZunSpyiLwyiuvGEqVKmVcFuotW/u+4rjV5sU+GThwoOGrr74y/Pbbb4Zp06apDAja88gCFhISkqF9QERERM7x0UcfpWo/9O7dW2VriYiIcHhZ+oxAWqbQHTt2pJnv1KlThtKlS1vNNon3R3ZGrU1uqS+ATEFoj2O+AgUKmG3fod2K9pi1tp0+o+XEiRPt2j5kKkI2SXv3B9qB5rJQ6T8DjCJFphlkIzLtEyBTTd26dY3zoq9hqa1oLdPrzZs3jVlZ0SeytG/0o54xDR482Gy7Vz8yFeudEcjYpGVWRXt7+fLlaeZBO1Lr82D6/PPP0/1+2KfO6lvs3bs33euh7y+Zy4Jrqk2bNsb5t27dashMw4YNM74X+gjmIGOrNs/ChQsNffr0sZrdCtmI7e2LYbSyto+xbPQtunXrlqrvwexIREREmYN9BfYV2FdwfV/BEe3bt7eZiUhP60tjunTpUobeGxmOihQpopY1duzYVM/ZkyEJfU1vb29jP9XatQhAZRVtmVWqVHGrfUGUHTAgiYgyzbp161IFDTmLPiAJJ9CdVYoJFyJwYQHLxYlQWwFJKJ92584ds/Nt37491UlQayfLR4wYYZwPgVOmXnzxRePJT1tBPTg5qi3LXACUvjGGQKmMBCOBVhajV69ehsyG9JXaum/cuNHiSX5cjNLKaeACSEaYBqBlZLKnIWopmEtbBhrZzp7fGuzPSpUqqWU9+uijaZ5PT0ASgrws7aPAwEDVgcD72vLFF1+oshGWloX1XrBggdnAPCIiInINBK6g9JXp/22cAKxZs6YqvzRjxgyrgTeWAnCslVFbvXq1cb4HHnggzfNTp041Pv/nn39afd8NGzYY58VFk/TSgjwsBZCbbp+1sszm5jcXnAUYAKG1lzEh6EhLbW8K/Qltvvfff9+QXijBpi3ngw8+sBmQhL6jpfJu6LNpAzrQ3ssIlH2zlnZfc/jwYWOJcAz8sLS/bNGXw87ohP2VXmjXa8tBQKCz50+vf//9V/0WaOXaLJ2Y79+/v3F9qlWrZpwffW6Ubvzll19U0JLWt8eE8unWSgaaW7bp1LJlS/X9JyIioszBvsJ97Cukxr5C1vUVHNGwYUPje6LP5Oz5rRkwYIBaDgYWYQC0owFJoA2cwfTNN99YnO/y5cvqWpM2b9GiRd1qXxBlBx6uztBERDkXSpFpbJUhSK/WrVs7rRSTn5+f1K1bV91HSn5bUIJOXxZOD+nhUVpN88ILL1hcDkolaJCiUQ8xHyhzACglYKv8GMq6aSUE/v77b6vzYp08PDL2bwClDOD06dOSkJAgmQll8TQo+WAOSnCgnAP07NlTperP7qKiooz3UR7Alnz58hnva/sivd599105d+6c+m6grEpme/DBB1VZRf02WIIyIx9//LHF3xasN0qVoDQJERERuQf8j9+4caOMHTs2VbsGJbiOHz+uUq6jjVq7dm3Vxv/999/tWi5KounbiqYeeughqVWrlrr/33//qTJkenhfQIkwtCGtQVnk0qVL29XetgbtHkDpNpQEswalq2ytlx5SxD/wwANmn0OpOX2J3+eee85iantr/RRHVKpUyVhazp5+1siRI1W5OXPQLtXW//z58xIXF5fu9ULJa8D2o7SdJXXq1FFl3ODy5cuqFGB25sr+hSX4Tj7++OPqtwA++OADKVu2rNl5UZJNg/KHRYsWVZ/JzJkzVR/9ySefVOU+UA4RJRdhz549Mn369HSvH0qlo4QjfiOIiIgoc7CvcB/7Cpaxr+A+XNWvWLlypfz222/q/owZM1QJ8fTA9QXNhAkTVOlzc9uI/kV8fLzxMZRjNzefu/WxiNxJ9i58T0S5HgKS7HX37l110vmPP/6QgwcPSkhIiGoomKsXixPNtjRv3tzicwgKQjAMTqwWKFDAeAHE0slNTVhYWKrnjh49Krdv3zaefF++fLnN9UIDDCdpcVHHWfvOks6dO6t9ivfq1KmTvPrqq+rWnoASRz3xxBPyyiuvqIsOCDz65ptv0jQ2586da7xv7aKUvSZOnKim3Agn7bW63ZMnTzaezM+oGjVqGL9zqJOMwMVdu3apzxOdCUx9+/ZVnyW+O5bW7ZFHHpErV66oID0ETuF4RoDgtWvX1DLwueF7jvmwbHRWiYiIyPUQ0D5t2jR56623VJtu/fr1smPHDvV/Xe/QoUMqQGHw4MHy008/WQ2kRzvAUgCLPpBIC6pB8IIW4BMeHq7eS2uX29veBmvtbWwXTpLivS5evKhOMqLtYw62HUEVliAwKE+ePGIva/0U0/5Hs2bN0tVP0cMJWQyiWLVqlRw+fFgFWEVHR5ud155+lqVgKo3WLkWbEv0eLdjJUWiDAgLg9Ntq6fjB9gE+U1vraE5QUJDZvmduh2Old+/ext+A7t27y7hx4yzOrwUtadBnQdCYqSpVqsi3335r/K5/9dVX8sYbb1hdF3xntYsbMTExKuhtxYoV8umnn8p7772ngprwvBagRkRERM7FvkJa7Cukxr5C7oZ+tXae/7HHHnNo4I4pBBph4DsGTaFPi0DAQYMGSZs2bVRQEfr7P/74o1y6dEkNssEAaMjoIH+i3IgBSUSUafTZafSjGJ3J3iAJnBhHAwWZfOxhLsrZlK3sOxh9DIGBgVYvIGjzgekI3+DgYOP9JUuWqMle1i4agDMCTKZMmSLbtm2Tq1evypYtW9Skjbpu2bKlOnGPSZ8tKr2QCefRRx+VBQsWqJPWuIA1dOhQ4/MXLlyQzZs3q/ulSpVSI+FzAn3QlT0jwGNjY433EcSWHsh2NWzYMHXRDBepRo0aJZkBo9GLFy8uPXr0UNM777wjH374ofps8Z3RRq3r4YIhOgXYTnQSNmzYkGrUQfny5dX64vPHuiPg6cUXX1TzOiubGhEREWUc2tLIzoMJEMiP7EXIOoTgFq09Pm/ePBVYgHaCJXjeFv08aLtqcHJRC3DYunWrmjLS3kaAEwKpHMmeZKvv4Wi73d5+iq15rfVTNJs2bZKBAwemyTqVkX6WteAse9fLFnxOWh81I8dPduSK/oUleP9evXoZg8PQh1y4cKHV/rN+HTAgAVmCLUFwEzKa4TPDhIsKNWvWtPuCKILVMGFwDNYNFwRx0QNBaexbEBERZR72Fe5jXyE19hUyDwaW4FqPJTjv3qhRI5f2KzDAAINcChUqpAYcZPTaBK63oT+7evVqlQUJAUiY9HCtC5UYMBBfy9Dszn0sInfEgCQiyjRaKQMtWCQxMdFYTsxZ7MnEgwxDaCyEhoaqv8uVK6eCH5CppVixYiqYQTvh+fbbb6usRKajLs2xNxI6IxHTOFGeXvo0kuY4I4sRRvnu379fpdT/5Zdf1El9ZKL6999/1YSRpAg4QcrLl156yaGR3eYg6xECkgDR6/qAJPytjTjGSHpLpSeyGzRm8b3B9wf719b3yBmlEvG5IfAH7/P9999nWdT/pEmTVBASSi+gRAuyGJhmF3vzzTeNDXZkV7CUAhWjFpCxa/z48SqwatasWWqENBEREbknZLlBZkNMaFviVjsZirYJ/q9bar9qZYSt0Wde1KdTz0h721zJYmR6RHYk7cQigheQ0REB81hPrV2FLCsIvABLmZPS2253pO2WkXYeBnsg4ENrm1WvXl26desmVatWVYMy9O20Z599Vm7cuGFzWzO6TvbSp8XPyPGTHen7CLbKBWZmKXb0V/v06aNGJAMGEyALlaUsqebWoV69elb7Ruh/4qKJFkSGEon2BiSZlk3ERQiUhMP3Hhlkte8vERERZT72FSxjX8H5cmtf4ciRI2pAuCVDhgxJVaEiq/sVGDyknd//+OOPU11/TC+sB/ogqLiA60sYJIV+K75X6DcgixIGUekHLpnL0OsufSwid8WAJCLKNPiHjRPRCAjCSeoDBw6oaOKshnJNWjASGk2zZ8+2eNLyo48+Eneij6xGWSoEbLgbBBx9/fXXKjgEpbS2b9+uLh5pqS6x78eMGaMCvRDckhEdO3ZUAWUYyf7PP/+oDFJa6QM0GDX6QKWMOHHihJqcAaU2bI3gsHRBpnLlynLy5El1AQcjALDNliD4T1OtWrV0rSu+I9pIcJQowGQLMhtpXnvttVQj1x3ZVpQBREAS4DPWByQh2E1/gc9aiRFAICICkkAbdU1ERETZY0Q0gtArVqyogrFxUhf/y9u2bWt2fpRWskVfQkzfxtbfR1C7vk3pKGQL1doqyJ6ybt06NQDCHATvZ3c4CawFI6EEHwLJLA1AGDFihLgTHFfOOH4cgfdxJHOWNaajkx2h7yPoM/JmZv/CFIJ6+vXrp0YiQ8OGDWXNmjVqpLMtCHzTZ0iyRT9PRgIQ9Rl4tcy8RERElPXYV8ge2FfInn0FR6BvgIy59vQr0P/SSjQjgCs91TuQuQjXgRAshKAf/fUIS/2X+fPnGwc6IZOxpf6MVsHBEq38OzRt2tTl+4Iou2FAEhFlGpyMRkCAVnYJ//xdEZCkXRRAENIXX3xhdQSlvrHiDvSNEQSiuDOUZWvRooWaxo0bp0a8otwGavoikOSHH35QpbPq1q2b7vdAwAqCytDY1IKQ3nvvPXXxR6vhi/dH9itnwMh1ZwWBoUHarl27dL22Tp06KiAJ9u7dazUgCUFh+telh5ZpCsFY1sqj6OnnGz16dLoCkkxTlJqWekRHQ8tEgPlsZdzSX3zQX0QiIiIi91e2bFl1Uk878WetTNaZM2dsLk8/j34kpTPb21q/QxvoYCkYyR37HRnZXgxQeP/99y22zTDCGINU3FVGjh9HYKCGtRHHjjAdnewIfR8BfQtrMDpYO1ZxPOOzziicgEcJtD///FP9jf4hgvfMlT4wB1mRHAkw0s9jTwBTevopRERElLXYV3B/7Ctkv74Crl1o1wXS06+wNkgcyQq0bLkYgJyeShraumFQDK4J2UNffg3rm94BFhg4rWndurXL9wVRdpM1NViIKNd6+eWXjfd/+uknl5x4DwkJMY6esJb+EKXHcMLVnWCkqDZKdMOGDXaVknMXPj4+qsQagpCcORJc35ibN2+eaojqG9h4z5yma9euxvtr1661Oi9GFpsbxZtd6C/2mGaU0o+YRnCSrXrM+t8bfP+JiIgoe0F70p6sNBjxaK58mp42WtF0RCPaG1pGRqRnR4bPjPY7ABkuLUHgvn59sitte5HJylqZNVyMcOd+DDK56j87R46f7AgXGrTBAxjYoWW5Mkff93BG3wIn3gcNGiRLlixRf+O7h+PDkbZ6mzZtjGUxUGZan+3KFPqK6Oc7I8OTtX4KERERZT32Fdwb+wo5X265ZoHrECjpBri+iLLTuXVfEKUXA5KIKFM9+OCD8vDDDxtHxmIkpL4Gry3IaIQSYBmh1flFlLm198aoXnfj6emp6tRqwRVaKa3sRJ/Nx9rJYnvh4g5OQgOyIqEBt3jxYvU30nX2799fnGXixInqJLYzpvRmR4JHHnnE2MlG+RKtBKG5Os8olafVMrZU1sQWpBW1Z5v09I+nt+4xshKgZrP+98N0VDJSzmoX8pYuXWozw5XGFdnZiIiIKDVbQR+m7ZHDhw8b/9aXcTWF7DvWRoAi9T2CTrRsmmgnmY4g1dLkf/LJJxnud8DZs2ctzjdr1ix1UjO707YXbXJLI2kRgDJ58mRxZ1jHr776yuLzyNKlnVRG+ej0BiRppaadMaU3O5IW3Kf10RGAZ2lZeB+UP9dktJ+FoLRnnnlGFi5caCy9hkE3jmZdwnHXq1cvY/YjbXnm/PXXX8ZyCAicy0hAkr78uGk/hYiIiDKOfYUU7Cu4l9zWV3BE1apV1YB6OH36tLEcsykMKkb1DA1Kp6UHtsue7ddfE8HAEu1xXGNJD1QD0QZxvPDCC6n6/a7aF0TZDQOSiCjToawW0qjCjh07pFWrVmr0sTW7du2SLl26yNixY1XgQUZojUA0Ot5+++00z+NxlJtavny5uKMJEyYYAzxeeukllRXIGgSrfPDBB2q0aGa6du2avPrqq3L+/HmL8+CiDj5/Tf369Z3y3vosSMOGDZOoqCh1/7HHHkuVRSenQHmEUaNGqfvYVmSJMs0OFBYWJoMHDzZeDMKxjoA2cxAchVSgmLKig/LGG2/YzI6Gi3ao04xjBtBxqF27dpr5ENSoz8Bm6Tj/5ZdfUqVkfeqppzKwBUREROQMaJcPHz48VYlZS0HKffv2NaYxRxCRtYxDgHbp7t27zbYxEAShP5loCicVK1SooO4jIOmzzz6zmtEHQRA4Ka0v0aZtn36wA8oWm1qxYoW8+eabkhNo24sssxhIYgpZq0aMGGHz83YH+My1Ua962LYBAwYYB1ag/WmpjZ2doP+rlQYYP3682TY1juGdO3caP+vu3bubXRb6E1rfwtIgDPRRnnvuOWNftkqVKmoghWlwoL1QokErxT5mzBhjwKHpdx/lwzWvvfZamnlQNu7333+3OnAGvwVTp06Vr7/+2viY1jcjIiIi52FfgX0Fd5Xb+gqO0JdOQ9v74sWLadrS6G9rj6Ofry9vZjo4XOtXWCt55ky4Tmmu3w54/JVXXjH2YWrUqGH2+mJm7AuinCal905ElImQzhwjH3v27CmnTp1SJztxUaF58+bSuXNnFf2NIBKMbMZJQ2S80Y+GziicLERgghbNjhqtSKuIk5+XLl2S//3vfyqNO0ZdI8MOary6EwRzIdMLRoGiEYQR3NOmTVN/I/Ia64yLIti3aEChLBq2tX379pm6XlgXnJjFhA4jaufWrFlTBU9hfU6ePKn2rTYiFc8jGM0Z+vXrp0rBITgHgVE5uVybvkGL78aJEydUhH2jRo3UBb0yZcqo8gHfffedOp4BFwKeffZZcRdYN3TcHnjgATWaGKOhcZygs3b16lVVZgWZkbTgQ3w3LWUDQ3DTokWLVCAcMgs0a9ZMjdZGABN+R3A8oIOITAga7IvsXlqDiIgoJ8D/+jlz5qgJAQnIetmgQQMVfI2SXxgVjQEMGCigjUBENpeZM2daXS6yvaxbt05atmyp2spod+JEMAKU8F764HVMplD+Ce+J9gQyxrz++uuq/YJ50UfAOuBxZALCwInNmzerbZk/f36q5Tz66KOqbYb2L+bDaxE8X6lSJblz545q7yAgCe+HZWtlq7IrtMex3wEnarFfkKoe5bcwKhQnbnGLfgluEWjmjtB2Rh8R/Sv0M7ANGPWKfivapFpZb7Q79SXJszOM3sVxPmXKFNV3QxsdfQtsI74vODa19jSOf312oPR46623jO17b29vtR/xHbEFg5TMjUBGf+Kjjz5SfQP0CZANFYGHOM+A3xIsW//dx2eKgChT+E5jEBTOWeC98HtUqlQpYx8bI97x26DPeIagxszuaxMREeVG7Cuwr+COcmNfwRG9e/dW5+aRtRQDknHNAu3uunXryq1bt1SfUGv3o52N61ru5MMPP1QVWnBOAZ8h1hEDptEPwDUIZG4G9PMxmMHX1zfH7guiTGUgIsoiYWFhhpEjRxp8fHyQwsXmVLJkScOMGTMMCQkJqZbz008/GefBfXvMnDnT4OHhYfG9atasaTh9+rShbdu2xsfMee+994zPb9q0yep7VqhQQc2HW2uwHG2ZWL4lO3bsMFSqVMmufVewYEHDoUOH0izD1vY5Ijg42K51wdS+fXvDzZs3Dc709NNPp3qPoKAgQ3JysiEnO3/+vKFhw4ZW93WnTp3Ud80a/XFg73fIHP37WuPv72/3sdKuXTvDmTNnrC7v3LlzhiZNmti1vNGjR6f5DSEiIiLX6Ny5syFPnjx2twtq165t2L17t11t6Pnz5xvy5s1rcVkPP/ywITY21ur6nThxwmZbS5vwXqtXrzbbZg8ICLD4usKFCxv++usvm/0Ke/sI6Zl/yJAhxnnRvrRGmw/tR3PGjx9vdT+1bNnSEBoaarNv5Mg6OTKvJXidtgws7++//7baZm3atKnT+zOuhr7TmDFjrH4nixcvbtiwYYPV5ej755aOE33/w5HJ1uf70UcfGby9va0uo1+/fobo6Gizr58+fbrd6+Ln52f44osvHNjDRERE5Aj2FdhXYF8he4qLizMMGDDAalu6cuXKhoMHD1pdjr6PjD5aeun7Hrau4XXv3t2ua1u4HpGV+4Iop2GGJCLKMsiIgtHNKEG2ePFilTUJkcYY0YioY39/fylfvrwxHTyikrU07BmFFIkYBYqo461bt6qI5ICAADUyG6kREalsbuSlO0F2GWQdQoQ1orEx4hsR+CjdhcwwGHmNbezUqZPafxh5nZlQ1gKZeVAjGVmZkNUK6SYxCjVv3rwqahwjVQcOHKhKcTkbsiH99NNPxr8xGl4rO5BTIZsYyiagLAKyZqE0AUq1YUQvPnuUJUPdYXfbDxhFguMEow204wQjjpG5AN97lGDB9x7rjswGtlSsWFFlA8P3ACMVUAbk+vXrKpOCn5+f+i4gGxcyEtSrVy9LtpGIiIhsQ8YVZMnBLTIkHjlyRI04RLsAsS/4P442JkYSYnRht27d7O4PDBo0SJUHRkZUlIJCFka075HxBG0CtEltQdYVZEtFFqOlS5eqbE1oY0RHRxvXDe/RoUMHNUIW/QlzbfaDBw/Kp59+qrJaIoMlsq2UK1dOtdGRvRV9Hnuyw2QHkydPVpmuvvnmG9VOxWeJtikyp6LULlLtO6tPl5mQuRdt1i+//FJlssJxikw+yHKFYwsZN7PDdjgCfYbp06erNjgyIG3ZskV9bzDqF+3pRx55RPWj8Xm6K5xbwHpi/bXfF5QKRMZV9CuQNQnfV0uwfSiTsGnTJtW/RiYvlEBHNmD0p5G9Df0JHB8Y7RwYGJil20dERJSbsK/AvoK7yo19BUfgWtCCBQvU9RlUKsF5e7Sp0YdGhQ9klsI+yuzrVemBMtW4LvHPP/+oigzI2oxrFshghCyy6APgvERu2BdEmSkPopIy9R2IiIiIiIiIiJwI5cG0skkobTtx4kTuX7IbAuEQ5A44WYyAfyIiIiLKGdhXoIxgX4GIyLk8nLw8IiIiIiIiIiIiIiIiIiIiIiLKxRiQRERERERERERERERERERERERETsOAJCIiIiIiIiIiIiIiIiIiIiIichoGJBERERERERERERERERERERERkdMwIImIiIiIiIiIiIiIiIiIiIiIiJwmj8FgMHB/EhERERERERERERERERERERGRMzBDEhERERERZTsxMTGyevVq+fDDD6VPnz5SoUIFyZMnj5omTpzolPcICQmRcePGSfXq1SVfvnwSGBgorVu3ltmzZwvHdRARERERZQ/sOxARERERuYaXi96XiIiIiIgo3Xbt2iUPP/xwpu3BvXv3SteuXeXWrVvq74IFC0pkZKRs27ZNTYsXL5Y///xTfHx8Mm0diIiIiIgo49h3ICIiIiJyDWZIIiIiIiKibCkgIEA6duwor732mixYsEBKlizplOWGh4dLjx49VDBSjRo1ZPfu3SoYKTo6Wr755hvx9vaWtWvXypgxY5zyfkRERERElLnYdyAiIiIiynrMkERERERERNkOSqfdvn071WNvvvmmU5b9+eefy/Xr11WZtlWrVknFihXV48iG9MILL0hERIRMmDBBvv/+exWUVK1aNae8LxEREREROR/7DkRERERErsEMSURERERElO14enpm2rLnzZunbgcMGGAMRtJ78cUXVQm3pKQk+fXXXzNtPYiIiIiIKOPYdyAiIiIicg0GJBEREREREd1z8uRJuXjxorrfrVs3s/sFwUgYZQ1///039x0RERERUS7EvgMRERERkXUMSCIiIiIiIrrnyJEjxn1Rp04di/tFe+7YsWPcd0REREREuRD7DkRERERE1nnZeJ4yKE+ePNyHRERERORyBoPB1auQLVy9etV4v0yZMhbn056LiIiQqKgolTXJFvYNiIiIiCi7YP/BtX0HYP+BiIiIiLJ734EZkoiIiIiIiO6JjIw07ov8+fNb3C/65/SvISIiIiKi3IF9ByIiIiIi65ghKYuExyZl1VsRkQkfL8ZeErlaQNPRrl4Folwr7sAMV68C2TnKXBsBzdHoREREROQqzMrjPth/ICIiIqLs3nfgVXoiIiIiIqJ7/Pz8jPsiJibG4n7RP6d/DRERERER5Q7sOxARERERWceAJCIiIiIiontKly5t3BdXrlyxuF+05woVKiQFCxbk/iMiIiIiymXYdyAiIiIiso4BSURERERERPfUqVPHuC+OHDlicb9oz9WqVYv7joiIiIgoF2LfgYiIiIjIOgYkERERERER3VOtWjUpX768ur9mzRqz+yU6Olq2bt2q7nfp0oX7joiIiIgoF2LfgYiIiIjIOgYkERERERER3ZMnTx4ZPHiwuv/bb79JcHBwmn0zY8YMiYqKEk9PT3nyySe574iIiIiIciH2HYiIiIiIrGNAEhERERERZUthYWFy8+ZN45ScnKwej4mJSfU4gof0Jk6cqC4eYDIXcPTqq69KyZIl1XK6d+8ue/fuVY/Hx8fLrFmz5J133lF/P/vss2pUNBERERERuTf2HYiIiIiIsh4DkoiIiIiIKFtq2LChFCtWzDhdunRJPf7ZZ5+lenz06NEOLdff319WrlwpRYoUkWPHjkmTJk2kUKFCUrBgQRk1apQKTEKptunTp2fSlhERERERkTOx70BERERElPW8XPCeREREREREbq1x48Zy9OhRmTJligpOQrBTgQIFpE6dOjJkyBB55plnxMOD4zuIiIiIiHI79h2IiIgyV2JiokRGRkp0dLQkJSVxdxNlEk9PT3UO3M/PT7y8nBNKlMdgMBicsiQyv4Pz5FG34bH8cSRyFR8vXiwkcrWApo5lJyEi54k7MEPdstmfffoO/KyIiIiIyFXYJs0++FkREVFuEBsbKxcvXpTk5GRXrwpRruHh4SHly5eXfPnyZbg9ygxJRERERERERERERERERERE5FaZkfTBSMjY4u3tbQyCICLnQVBRQkKC+t7hO4fvXuXKlTOcKYkBSUREREREREREREREREREROQ2UKZNC0YqUaKEBAQEMBiJKJODksLCwiQkJER99/AdxPcuI1jHiIiIiIiIiIiIiIiIiIiIiNxGdHS0ukWGFgYjEWU+ZB/Dd03LiqR9BzOCAUlERERERERERERERERERETkNpKSktQty7QRZW1QEr5z+u9gRjAgiYiIiIiIiIiIiIiIiIiIiNwyQIKIsud3jgFJRERERERERERERERERERERETkNAxIIiIiIiIiIiIiIiIiIiIiIiIip2FAEhERERERERERERERERERERFZFRwcrEp6DR06NEN7avPmzWo5EydOdKs9HhQUpCZyDgYkEREREREREREREREREREREbmhpKQk+eGHH6Rt27YSGBgo3t7eUrx4calXr54MHz5c/vzzT1evYo42d+5cFTyFW3KMl4PzExEREREREREREREREREREVEWBCP16NFD1qxZI4ULF5bu3btL2bJlJT4+Xo4ePSr/+9//5MSJE9KrV68s+SzKlCkjx48fF39/f8mJNmzY4OpVyFEYkERERERERERERERERERERETkZhYsWKCCkerXry///PNPmkCgmJgY2blzZ5atD7Iz1ahRQ3KqypUru3oVchSWbCMiIiIiIiIiIiIiIiIiIiJyM9u3b1e3Q4cONZuVKH/+/NK+ffs0j9+9e1c++eQTqVu3rpqnUKFC0rp1a1m0aJHF99q1a5f0799fZUHKmzevlCpVSrp06ZLqNcHBwap8GdZH79SpU/Lmm29KkyZNpFixYur1FSpUkGeffVYuX76cwb0gMnHiRPW+mzdvTvOcpXXC33gcz3/33XdqX/j6+kqJEiXUeoWHh6dZVlBQkJo07dq1k6efflrdxy2Wp01YLlnHDElEREREREREREREREREREREbqZIkSLGgB97oZxb165dVUYlZDN64YUXVCalxYsXq4CjAwcOyOTJk1O95ocffpCRI0eKp6enKv9WtWpVCQ0NlT179sjMmTPl8ccft/qeS5culW+//VYFRz344IPi4+OjSsrNnj1bVqxYoZaDQCdXeP3112Xt2rXSs2dPFWC1adMmtb1nzpyRjRs3Wn0tgppQKu+PP/6Q3r17S4MGDYzP4XGyjgFJRERERERERERERERERERElC30nbVdroXHSXZQyt9XFo98MN2v79Onj0yZMkUF+0RGRsqjjz4qjRs3VtmHLJk6daoKRurWrZv8+eef4uWVEhby3nvvSbNmzeTjjz+WHj16qMAhOHbsmIwaNUplUdq6davUrl071fLsyXD01FNPydixY1VmJL2///5brceHH34os2bNElf477//5PDhw1K+fHn1d2JionTo0EEFJiErFPaJJVrWJQQkPfLII2myMJF1DEgiIiIiIiIiIiIiIiIiIiKibAHBSFfuxEpu0LBhQ/nll1/k5ZdfVreYIDAwUNq0aSPPPPOMyvyj9+OPP6qSYtOmTTMGI0Hx4sXlnXfekeHDh6vMRVpAEgKFEKSD50yDkaBs2bI219NS9iNkJMIykaHIVd59911jMBJgn6D8GoKvbAUkUcZ4cAcSERERERERERERERERERERuR+US7t48aIK6kHQELIbJScny/Lly1V5tSFDhojBYFDzIosSSpGVLl1alWszhcxAsH///lQZhACZjNIL749gqU6dOkmxYsVU0A+CojAhO9GVK1fEVZo0aZLmsXLlyqnbsLAwF6xR7sEMSURERERERERERERERERERJRtyqDltnX19vZW2YYwQVJSkixZskRlSJo3b54q5YaSYuHh4SnvW6qU+fW59/idO3eMj2n3LWU5sscrr7wiX3zxhVp+165d1bLy5cunnps7d65cuHBBXKVw4cJpHtMyR2E/UuZhQBIRERERERERERERERERERFlC4tHppQay808PT1V5iRkH/rwww9l48aNKiDJ399fPX/9+nWzr7t27Zq61ebTB+wgi5G5rEq2hIaGyldffSV16tSR7du3i5+fX6rnFyxYIBnl4ZFS/Aul5Uzpg6vIvbBkGxEREREREREREREREREREVE2owX/aCXb8HflypVVcNHp06fTzL9p0yZ126hRI+NjDzzwgLpdvXp1utbh3LlzqoQcsjeZBiNdvnxZPZ9RAQEB6vbSpUtpntuzZ49kdvAXMJuS4xiQRERERERERERERERERERERORmkF1o3bp1KuDHFLIg/fDDD+p+mzZtjI+jjBsClF577bVUQTQ3b96UDz74wDiPZuTIkaqEGZ47duxYmvdBUJE1QUFB6nbbtm2p3i8qKkpGjBhhNquRo5o1a6Zuf/rpp1TLQ4DS+++/L5mpSJEi6vbixYuZ+j45EUu2EREREREREREREREREREREbmZnTt3ypdffiklS5aUVq1aScWKFdXj58+fl7/++ktiY2Old+/e0rdvX+NrXn31VZXt6I8//pD69evLww8/LDExMfL777+r8mqvv/66WpamVq1aMnPmTHn++eelYcOGanlVq1aVW7duye7du6VQoULGzErmYN0GDBggv/32mzRo0EBlSgoPD1eBVL6+vuqxAwcOZGg/NG/eXAVdbdmyRQUndejQQUJCQmTFihXStWtXs5mTnKVFixaSP39++eKLL9Q+wfbCiy++mKr0HaXFgCQiIiIiIiIiIiIiIiIiIiIiNzNu3DgVHLR+/Xo5dOiQrF27VuLi4lTWnnbt2snAgQPVlCdPHuNrfHx8VDDQtGnT5H//+598/fXXKgMSgpMQVPPEE0+keR9kMqpTp458/vnnsnnzZlm+fLkULVpU6tWrJ8OHD7e5nnPmzJFKlSrJwoULZcaMGVKsWDHp1auXyl702GOPOWVfIMAKWZ9wi23Cfvn0009VANSiRYsks6Bc3JIlS2TSpEkyd+5ciY6OVo8PGjSIAUk25DFoxQQpU2hf/PDY+6nJiChr+XixOiWRqwU0He3qVSDKteIOzFC3bPZnn74DPysiIiIichW2SbMPflZERJTTXbhwQWX1QWaaChUquHp1iHKNC3Z+9+xpj/IqPREREREREREREREREREREREROQ0DkoiIiIiIiIiIiIiIiIiIiIiIyGkYkERERERERERERERERERERERERE7DgCQiIiIiIiIiIiIiIiIiIiIiInIaBiQREREREREREREREREREREREZHTMCCJiIiIiIiIiIiIiIiIiIiIiIichgFJRERERERERERERERERERERETkNAxIIiIiIiIiIiIiIiIiIiIiIiIip2FAEhEREREREREREREREREREREROQ0DkoiIiIiIiIiIiIiIiIiIiIiIyGkYkERERERERERERERERERERERERE7DgCQiIiIiIiIiIiIiIiIiIiIiInIaBiQREREREREREREREREREREREZHTMCCJiIiIiIiIiIiIiIiIiIiIiNzK5s2bJU+ePDJx4sRUj7dr1049Tu6NAUlERERERERERERE5BZi4hPlcliMJCYlu3pViIiIiIjcAgJv9JOnp6cEBgaqoJy5c+eKwWBw9SoSmeVl/mEiIiIiIiIiIiIioswRl5AkZ0Kj5HRopJy8HiWnQyLlVGikXLodq54vmNdLGlcIkOaVAqV5xSJSr6y/eHtyfC0RERER5V7vvfeeuk1ISJAzZ87IsmXL5J9//pE9e/bIN998I7nJvHnzJCYmxtWrQTYwIImIiIiIiIiIiIiIMsXdxCQ5dyNaToVEyumQKHWL6eLtGEm2MpA76m6i/HPqhpogn7dnSoBSxUBpVjFQGpQvLHm9PPmpEREREVGuYVq27N9//5U2bdrIzJkzZdy4cVKxYkXJLcqXL+/qVSA7cEgJEREREREREREREWVIQlKyynL016FrMm3dKRn5y17pOHWz1Hp3rXT7cqu8/NsB+WbTGfn7WIgE37IejGRObEKSbDtzU6auOyX9v/9P6k78W/p/t0O91/YzNyU2PinHfIK3o+NlT/Bt2Xwy1NWrQkRERERurGXLllKjRg1Vsm3v3r1pnt+5c6f07dtXSpYsKT4+PlKuXDl57rnn5OrVq2aXd/v2bXnrrbekTp06kj9/fvH395f69evLm2++KdHR0anmPX36tAwePFjKlCmjll26dGn1Nx43F0iFUnObN2+W//3vf9K8eXMpWLCgBAUFGecJCQmRYcOGSYkSJSRfvnzSoEED+fnnny1uO8rVYZl6WD4ew/sdOHBAunfvLoULF1bb0rZtW9m+fbvZZV27dk2efvppKV68eKr31i/Plu+++07NO2nSJLPPX79+Xby9vaVu3bqpHk9MTFQBZQ888IAUKlRIrWvDhg1Vxqvk5LRlrFGi77HHHpNKlSqpdcVrcBz88ssvVvdTfHy8vP/++1K9enXJmzevDB06VLICMyQREREREREREeVCSckGlbkkLiFZ3d5NSBZfb08pUShvmpN6RESaxKRkuXA7JqXEmi7j0fmb0ZKQ5GCUkQk/Xy+pVsJPAgv4yP6Ld+Rm1F2L88YnJsvO87fV9JWIeHvmkXplC6sMSs0rFZEmFQKkQF73Pf2N9b9wK1rO3oiWczejVBapczei5NzNaLkTk6DmKVM4n/z7ZgdXryoRERERZQMIdtH78ccf5dlnn1XBJ7169VLBSAgWmj17tqxYsUL++++/VFmGzp8/L+3bt5cLFy5I48aNZeTIkSog5tSpUzJ9+nR5/vnnpUCBAmre3bt3S6dOnSQyMlItu1atWnLixAkVFPPHH3/I+vXrpWnTpmnWcerUqbJu3Trp2bOneq/w8HD1+M2bN+XBBx+Uc+fOSatWrdSEICG8Z5cuXRzeFyhh9+mnn0qLFi1k+PDhcvHiRVmyZIl07NhRBSohKEcTGhqq5sN2I9sU1gPBQ6NGjXLovZ988kl5/fXXZc6cOfL222+Lp2fqbK74PBB8hIAwDUrvYV+sXbtWrdPAgQPF19dXNm3aJC+++KIKKJs/f36q5eBzqV27tlrXUqVKya1bt2TVqlXy1FNPycmTJ+WDDz4wu34IYsLn1q1bN3nkkUdU8FVWcN8eGRERERERERFRJsMowjOhUbLl9E11YdgjTx7x9MgjXh55xOPerfa3p4fH/b8986h59X/rn/fEcjz1r/fQLUe/zHvP4fV58qgMI6ZBQnH3bu8mJktcQpK61c+Tet5kuZuQZLzVXoML3/rX4zbRQnoS/3zeUqOkn9QsVUhqlkq5RYAAgpWIKPdITjbIpbCYVEFHuH/2RpT6TcmIAj6eUrWEn1QrUVD9vuB+9RJ+qQIi8fuM4Jyd527LrvO3VODRtfA4i8tEMNTeC2Fqmrn5rPp9rVPGXx64V+KtSVCg+n3LStiGG1F37wUb3Q84wj68ZKNkHVy5E6syP+Xz4e8vERERUSpzuopEmM/y43YKlRYZtjZTFr1lyxYVCIQMRc2aNTM+jiAiBPMgA9E///yjshhpNmzYoAJtXn75ZVm2bFmqgBoE5UyePFnGjx+f6n0QMISMRlobF5mQIiIiVAASXqdZuHChDBgwQAXHHDt2TDw8Uhfs2rhxo+zYsUNlANKbMGGCCkYaM2aMCn7SjB49WgULOeqvv/6Sn376KVUWIGQwwj758ssvVUYiDbYV241goilTphgfx7ro96kt2D/Y7hkzZsjq1aulR48exuewzxAIhuxHmEfz0UcfqWAkbOcXX3xhDGJKSkpSwWQIYkKGq969extfc+TIEalcuXKq90b2IwQaffLJJ2ob9Z+3BtuI1xYtWtTubXIGBiRRrhITEyP/bv1HDuzfp6aD+/fJpUsX1XNvvvWujH/7PZvLCA0JkS+nfSZrVv8lly9dFN98+aRmzdryxKCnZPDQYRxFSuQkiKr+cvpUWb50iQQHn1f/hKtUrSb9+g+QUS+8qBpXRGRegxpl5eE2daVhrfJStXwxKRrgJ4UK+EpEdJycCr4ua7Ydkx9+3yphETFpXvvWcw/L288/bHPX1u41Uc5dumn2Od+83jKibyvp3aG+1KhUSr13TFy8nL10Q9ZtPy6zftssIbci+fEREZHL3Iq6q8r+bD19U7advinXIyxf4M6NwmMTjFlHNB55RCoWLSA1ShWSWqUKGQOWSvn7sh9MlI0zHYVG3lW/gdfDU6aQiDgV9INsR6dDI1XAY0b4entI1eIIOCqoAo5Sgo8Kqsw/tjKx4fnKxQqqaWDz8uok/qXbsfIfgpPO4TfqllwOi7WaBe7gpTtq+m7LOcHb4ferecUi0rxSoDQLCpSAAs45t4AgT+wzfdCRur0RLZF3EzO0bCy3VulCTllPIiIiohwDwUjhKdd4cxOtdBgy65w5c0YFFKGd/Pnnn6tsOZpZs2apeRB8YxqcgixByGqELEm4Fufn56fKvSFQCKXK3njjjTTvqw9iQdkzBEEhUEgfjAT9+/dXpca2bdumJmTx0UOQjWkwEtbz119/VethWhqtSZMm6j2slW4zByXMTEuSPfPMMyrwZ9euXakCeRYsWKBK0yGrkR5K1SHwCoFE9kL2IgQkIfhJH5D0999/qwxUKAuH9wJkn/r6669VOT0EYekzKuE+skkhqAr7Rh+QZBqMBLhm+sILL6iALwScYb1NIXNSVgcjAQOSKFfZu2eX9H3k/pffUfv37ZU+vbrJ7Vu3jJGOUZGRsmP7NjX9sWyJ/Lb4DwZKEGUQonS7dmonF4KD1d+IGL57967s27tHTQv/96us+nuDBAQEcF8TmTGkdwt5fkBb49+xcfESezdeihQuIC0aVFbT6CfbSb8x38vOQ+fN7sP4hES5HZ42YEmTaGFEdPlSAbJy1otStcL9dJ93ImPEL7+vNKpVXk3P9W8tfcd8J//uO8vPj4iIsgSyByFjBgKQtp6+IUeuRHDPOwhZPFBWCNNfh64ZH2c2JSL3FBOfaAwyun4vyAjBRtrfuEXmHkPGKqwZ+Xh5SJViyHZU8F7mo5SMR2UD8qlsc86AAKXyRfKr6fEm5YwZhHYZA5Ruq+AdS7CtR69GqOnHf1P6QVhHBCchSAlZlIr55bWaMQr77pyuxBoyHeH2anisU/YlgqYQrFWpWEGpVLSAVC5WwOo6EREREVHuMmnSpDRtZJQIQ6CLHoKLANmRUKbLFMqUIQsPMimhPBvKt0HXrl3TZDUytW/fPnXboYP50sJ4HMFI+/fvTxOQZC7jEIKbkFSkdevWxmAdvXbt2jkckIRAJnMl7UqUKCFhYWHGx1DiLDY2Vs2PgChTKB3nSECSVkoNGZIuXbqkyuTB999/r26RvUiDfX/79m2pWrWqfPjhh2aXly9fPjl+/Hiqx1B+DpmcEHiE+1h/vStXrphdliPZnpyJAUmU6xQOCJD6DRpJ/QYN1TThjXEScv26zdehhmX/x3qpYKRq1WvId3N+lkaNm6jIyZ9/nC3jX39FNqz7W958baxM+3JGlmwLUU6E+ql9H+mpgpFKliolc36aJx06dlKRwksW/y4vPD9CDhzYL88MGSTL/vzL1atL5JZ2H70gF6Ytk+0HzsrJ8yESHpXSIC2Qz0d6d2wgH499VIoH+snCaSOk3iPvS0RU2qwQ/x08L11HfOnwe8/+YLAKRrobnyBvTF0mv6z4T6Jj48Xby1O6tKwlX45/XMqUCJBfpgyTmj3ek7i7CU7ZZiIiIktl2BCAhAvVsQlJuXInIUggr5o8VaaS1Pfv3+a999zNqHg5cS1Crlopi+RoNiWUfatRktmUiJz1+3Y7Ol4FxmjZjELCU271j0XGZSwrjyXennmkUtGCUq2kn1QrrgUfFZQKRQqoEmlZDcE7jzYsqybA9u9Sv0kpQUqnQ6Osvv5kSKSa5u24oP5GAFAzZFCqGKiCgxCEqWU6QrCTs/6X+Pl6qaCjykULSKVimAqq26AiBVgek4iIiMjeMmi5cF3RH4Do6GgVdDRs2DAV5FKhQoVUAUK37iXX+Oyzz6wuLyoqpb18584ddWuu1Je5a+agz8ikpz2uLVMP2YAsLQ/BQuaYe40thQsXNvu4l5eXCsSy970tPW7NqFGjVCk9BDJNmjRJrl+/Ln/++afKPqUPCtI+o9OnT6cJNDP3GQHK2mEZCKpCABdK7yGICxmVgoODVeAWEjw4az9meUASdgp2FqKtEPmGDBa4cFysWDEVNTZkyBB59NFHrS4Dab+QXmrJkiUqLRV2TrVq1VQtwRdftF2CJyQkRD799FNZuXKlivhCVBgizfDe+MJZSvWLlFz2RM4hJRgORMqZHmzZWi5cTV1eZtI7E+x67ddfTFWBSzjmfl++UoKCKqrHccyOeH6URERGyPvvviVz5/wgo0a/rEpLEZHjfpn3sxw5cljdX7BwiTxwrzYsIrL7Pd5fBSYNfWqgrFm9SjZt3CDtO3TkbiYy8b+V91OO6iEwCM+F3IyQlbNGS4kiheTh1nXkt9V7nLIPkR2pdeOq6v5nP66T7xZtMT6XkJgkf/1zWKJj78rq716SkkULSatGVWT9jtTR/UREROmFC/SqDNupGyoTkr1l2BCEg6wYbaoWU7cIzElMMqhSP4nJ2m2yutWmxFS3yanmTzYYTF6fbDK/fjkpy9XP7+3podbBVxck5OvtmerWUkCR6Wt8PD3SnZnkTky8nLgeKcevRciJa5Fy/HqEnLweKXctZEnUYzYlooyVUUMGH5QiS8lkhNu7KUFHEbESEnFX4u34HmYUgosQWKgyHhW/l/GoZErgEX6n3FWJQr7Ss35pNWklOlMClFKmE9cjrGYy0jLBLdh10Sn7sHxgfpXpyBh0pO4XlKIFfVjukoiIiCgjhq3N1fuvQIEC0qlTJ1V2rVGjRipWAtl+UHEEtExDCLgpVKiQ3QE8lrLr6GnLRqCNOdeupWRVNpftyFwshzYf4kDMsfQ+zqDtG0vvbelxa/r06aMCmZC56t1335Uff/xRxdQ899xzZrcb8TVLly61a9nTpk1TMTso5WZakg6l56zFw9gqmZ1ZHIq8QdQUdpbG19dXpbbCgYnpjz/+kG7dusnixYuNB7seApiQUgvRWfoSPHv27FET6t8h2MlSCR7ULkSasFu6clkIcNJqEOJ9ETBlLagJ62zu4Hf1B0FZQ1970VG//Tpf3T7Wr78xGEnvuZGjZdqnH6soxUW//U8mvJO6xiUR2eeX+Sn/LNu2a28MRtJ7vP8AmfjuWxJ8/rz8On8eA5KI0mHX4ZS2GJQpYX6kQHqULHq/jbXvmPkT+HuOpIw8hoL5WXqAiIicU4Zt2+mbcuRquN3lcmqWKiRtqhaVVlWLStOgQGajMKNwfh95oFIRNWkQNIUMISpI6XqEHL8WmSnZlBpXCFSl4IhyW1Dlb7svyq//XVRlyDITshwV9/OVUv6+UsLfV0oWundfd4sJGdayuyIF80q3uqXUpAVb7g4Ok53nbqnfoqNXw1UQZUYEFvAxG3SEYKScsA+JiIiIyH3Vq1dPRowYId9++61Mnz5d3nrrLfX4Aw88oGIrtm7dKt27d7e5HMwPa9eulcmTJ1st29awYUN1u3nzZrPPb9q0Sd0iUMoeNWrUUHEjBw4cUAFUprEclt7HGfDeSEZy6NAhFXdiWrYNMSiOQvzM8OHD5aOPPlIBY8iUhLiWJ598Ms17IxAM5fKQNAevs+XMmTPq9rHHHkvzHMrzuSOHApIQjIQUUIi2QmBQpUqV1OMIMEJdO0R5oR4eorvmz5+f5rU9e/ZU8yJN17x581TUHjJd/P777+qLgjqCgwYNkr/+SluCBwdfjx49VDASPhwsH1mZUC7rhx9+kLFjx6ovyJgxY2TmzJkWt6F///4yd+5cRzabSE6fOimXLqVcWO3U5SGzewQ/JC1atpJ1a9fIxvXrGJBElA6oEbtj+7/qfpeHulkMHO3S5SH5/rtZsmH939zPROnQsmFl4/1zl1NnDsyI87plNapVXlZvPZJmniZ1KqjbpKRkOXDiktPem4iIcj6kJT97I0q2nEopw/afA2XYihbMqwKQWlcrKi2rFFUX4il92T6qFC+oJi37SGZkU/LyyCNNggKkQ43iaqpcrCAHkFGOdeRKuPy8PVj+PHjVru+MLQXzeknJe0FG2i2Cjkppf/v7SmB+n3RnT8sJwZada5VQE0TGJcieCwhQSinzdvhyuMpWZwoZ5yoUyZ8m6Agl3rBMIiIiIiJXefvtt1XGnM8//1yVC0Pyl9GjR8v333+vYiiqVq2qKlbpIcZi586dquwXNG7cWB588EHZvn27TJkyRcaPH59qfsRoICsTkr+0bNlSqlevbkwY07dvX+N8+BtBUHi/Vq1a2bX+CMRBsA5iPiZOnKgCqzRaUpvMgkQ3WvwI4l2w7ZqDBw+qmJb0ePbZZ+WTTz5RnwMS++Bv02AnVO1CBbEPPvhAXnrpJZX9CMFRptmmUJ6tVq1a6u+goCBjkBZibzSIk0HgU7YPSNq4caO0b98+zePYcGwgdtp3330nv/zyi4qcK1eunHEepIc6fDilBA/KtbXQleDBh4zApIEDB8qqVatUlqSOHVOX4MEXCOm48CFgnooV75fLeuGFFyQiIkImTJigvlgISjL9UhFlxLGj9y+o1qpdx+J8NWvVUQFJJ08c4w4nSocTx4+r/wdQ28p3Tfse4v/C7du3JTAwkPubyAYfby9VJu3hNnXknZEpIyLOXAyVv/5JGzQENSuXlD2/T5CKZYqq0jNXQ+/Itn1n5PtFW+XgyctmX3MjLEqWbzggj3RsIK8901luhkXJLyv+U6XivLw8pGvL2vLl+MfVvF/O3yDBV1KyXhIREdkqw7btdEoZtmvhjpVha40gpKrFpEZJPwa0ZKNsSggGQMAZpsmrTki5wHzSsUYJaV+juDSvyIxWlP0lJCXL2qPXVSASsvXYC8GVJf3zpgo2Kumf7/7f/r4qIIns5+frLe2rF1cTxMQnyr4Ld+Tg5Tvqf4kKQCpaUMoG5BMvNy5VR0RERES5V5kyZeT555+XL7/8Uj799FP5+OOPVYIXlAp75plnpHbt2vLQQw+p+Alk4rl48aIKGipWrJicOHHCuBzEeKDaFWIuEM+B+xgcdvr0afn777/VvIgLQeIAxH507txZxXn07t1bvR9Kxi1fvlwF3iCQx1qWJVOILUGMyBdffKGCkBDMhGCchQsXysMPP6yqZGUWBA4hDgb7DkFaCMzCey9atEi9N7bJkW2B8uXLq8xUf95bb9NybZp33nlHBT4hwxWyKXXo0EF9nqGhoWq///vvvyrTkhaQhIAzBJ/169dPBYKVLl1ajhw5ImvWrJHHH39c7S9341AP1Vwwkt6wYcNUQBLgQDENSNKWoQUj6Q0YMEClEDt//rw6QE0DkrToM8ynBSPpIXoMByrKZSFKbtKkSY5sGpFV1+/VuoRSpctYnK/0vecQIIdjEVmTiMh+165dTfN9MvtdK3P/uWtXrzIgiciKsP+mi2/etKk+t+8/K0MnzJX4hPvlePWKBfhJYKECcicyVgoV8JVqQSXUNPSRFvLpnL9l0syVZl83ctKvkt/XR7q0rCVfjH9cTXciY8Qvv694enrI4VNX5MPvVsncZTv4uRERURrxicn3yrClBCA5UoYNQUdtqhVTQUgsw5azsilduh0rc7cHqymft6cqtYfMSQggQAAGUXZxM+quLNh5UX7deVGuR1gOzmsWFKgy+JQqfD/TETK7sfxX5svv46V+YzAREREREWUXyGiEDENfffWVSt5SokQJVZmqfv36MnXqVFVGDUFFyHKEIBYEsyCYSA8xGPv27VOBOQjC+eabb1RGJAQhjRs3TooXTwnih+bNm8vu3btVVqH169erYJqiRYvKE088oYJskEHJEXgtgm8QDIVlIdYEy5g1a5Z6/8wMSMK+QmYovDcS4yAoCe+NqlzYX9gXhQoVcni5CAb7888/VdUvS+XrkB0Ky0cwGLI0rVy5UsUYIFgMnweyJ+lLvaFEHz5LZMVC1TFUKcNnvHTpUlX+LdsHJNmCA1KTlJSUqgQPDiDo1s1yCR5E5uGgwpdBD9F0iNSz9noEfiClGErG4fUMSCJnioyKNN5HDUtL8uW/n0YtKjKSAUlEjn7XIu37rumf07+GiNIKuRUheX28pWD+vGqCzbtOyltf/iGXrqcdjX32YqhMmL5MVmw+LMFXb0piYrJ4e3lKmyZVZdKLvaRxrfLy5oiHVJDRl/M3pnk9ApgGvPqDvDuyh7w0qL0aOVDY7/53FutQtHBBVSIi2UwpBCIiyj3CYxPkTGiUnAmNVLcIUkEwUky8/WXYUjIgFZVWKMNWiIEp2T2bEjIp7Tp/WzaeCJXLYbFmX48yfeuOhagJapculBKcVKO41C9bWAVCZRcYaYqsX0evRsjRq+Hq9tjVCImITZBihfJKKZUBJ1/KLUpuGW/zSUB+b2b9ykYOXrqjsiGtPHRN4pPMB98hG88jDcrI4AcrSO3S/lm+jkRERERE5N79R1tBNdHR0Wker1u3rgp0sVeRIkVU2TJ96TJLELQzf/58u5aLUmyYrClZsqTK6mTv9qNsmSktq5MlwcHBZh9HViItwY4eEupAzZo1xVH79+9Xt8heZQ3iZJ566ik12QMZnJDRKSP7KdsGJOk3Bge35riuBE+dOpZL8GjPmZbgQZop03ksvR4BSceOWS6XhVRfSEeGACeUe6tQoYLKxoSyb6ifSERERJST1Oj+nvF+sYCCMrBHM3l9WFfZOv9V+WT2Wvlg1l+p5v9t9Z40y0hITJIN/51QJdvWzxkjTeoEyVvPPSw/LdsuEVGpR3bXr15Wfp/+rJQuXliVd/th8TY5f+WmlChSSLq3qStvj3xYPnipt7RsVEX6vPStzY4UERFlb/idvxUdL6dDouTMjSg5ExKpbvF3aORdh5aFzCAo18UybDk7m1KPeqVlUi+DClJDYNKGE6EqUA1BS+akBPNEyNcbz0hgAR9pV72YClBCqT7/fGmzRLoKArGDb0XLkXvBR8furTfKEpoTeSNRzt1IezJZ/33QMucYA5Xule/C35iKFMybrQK0cmLGt9VHrslP/wbLgUt3LM5XpnA+GfRABRnQtJwEFPDJ0nUkIiIiIiIikatXr6rMUXqHDx9WGacQs9K2bVuHdhOSKaAMW2BgoMoalZs5LSDpzp07qh4hIFORPg0XPkB9dJkl+ufwGi0gydHXWyuXdfnyZfH09FRptTAfgp0wITMTahKOHDnSga1OiVijnM+voF+qjF+W0rLFxtwfwVnQ7/5riMjO75pf6u+aJfrn9K8hIutuhEWprEb/7jsrm38eJxOe7SZ7jlyQ1VvvB39bczc+Ud79ZoWs+vZF8SvgK+2bVZc/Nh5Mlf1o+TejpGTRQvLRd6vkw29XGZ8LvnJLZizYLEfPXpW/Zo2Wh1rVlsG9H5Cfl7N0GxFRTgk8QvkhBBqdVlmP7mc+CotJSPdyUYZNC0BqVjFQfL09nbre5J5wrqVqCT81Pde2soTHJMiW0zdUgNLmk6EWjykE9yzdd0VNXh55pElQgApOwlS5WMEsO4eDQJTToZEpwVJXUjIfoURdtJ3Zv+x9j4u3Y9RkCYKRSvjlNWZVSp1lKSV4qbhfXvH29HDaepFIaEScKsn2v10X5YaVwMsWlYrIkAeDpFPN4uLFz4CIiIiIiMhlUFatSpUqKgEOyrSdPn1alURD0p3vvvsuVaUwa/AalL1D2bmQkBD5/PPPrVaEyQ2cEpCEDwIppK5du6Y+DNQTdGYJnvS+Xh+QhLp8TZs2lR49ekjZsmVVUBIuaK9Zs0Zef/11OXv2rIwaNUrVPnzsscfs3nbKHUqWKmW8f+3qFYsBSVevXlG3eN5cQBwRWVeqVOlU36e69eqZ/65duXL/NSYRy0Rk256jF2T7gbPSunFVeeaxlnYHJMHOg+eN9yuWKZrquSe6N1XBSGCunBts3nVKDpy4LI1qlZdHOjZgQBIRUTaDLDWXw2KMGY+027OhURJ1NzHDy2cZNjLHP7+39KxfWk04BpFtZuOJENl44oYK9DEnMdkg/527rabJq05I+cD8xtJuzZ0Y3BYTn6jWISX4KEKOXA1X3wtLZbls8cvrpQKGkEEMZQ0zAvvqanicmkTMZ+hBjBa+dynl4VIClUr4+0ohX28pkNdTCvh4SYG8XpLfx1MK4javlxTw8VSPMZApdVDmvospZdlWHb6mjj9z8nl7yqONysiQFkFSvSQH1xAREREREbmD5557TpYvXy4LFixQcSaFCxeWrl27yquvvqrKwNnr999/V6XfUEJv/PjxMnbsWMntnBKQ9PLLL8vKlSvV/RkzZkg9CxeQXemll14yG8DUp08flWILwUrnz5+XcePGqcfsHTVnq8wIMyjlDLVq3y8VeOzoEalew3ydyOPHUi7oVq9RK8vWjSgnqVGzpnh4eKhA16NHj0jXh7qZnQ/fQ62erJZNj4gcczU0XN1WLpc6qCgjalZKCeANvR0pkdGpS7npnb10QwUkBZUu4rT3JiIi50pISpYLt6JTZTzC7bkbUXI3MX2BFnrF/PJK1eIF1ZRSqstP3RYt6MN+NFmFrD+NKwSo6bWuNeTqnVjZdDJUNp0IlW1nbkpcgvnjE5mE5m4PVhOCQlpVLZoSoFS9uAoAskdYdPy9EnEpWY8QfHT+ZrSktwItAoHqlCkktUtj8le35QLyi8e9MmsIdroeHqema7iNwG2sXA+/K9cjcBsnN6PMl3yzF9YdWXwwHZKU9qG9fDw9JL8xaMlT8vt4pQQt3QtY0gKa8Li6rz13L8gp1WvuzZvdSszFJSTJykPXVCDS4SuW91+5wHwy+IEgebxJORVgR0RERERERO7jvffeU1NGzZ07V03kxIAkRIVpGZGmT58uzzzzjNNL8Ji+3lJ2mvSW8ClSpIhMmDBBRowYIRcuXJD9+/erjEpEmipVq0m5cuXl0qWLsn7dWnn0sX5pdk50dLTs+Hebut+hU2fuPKJ0QKBoiwdbyr/btsq6tWvklXGvmQ0EXbdurbrfsVMX7meidKpYJiUYKCrachkJc5rVCzLeD756K9VzCCaEooULSD5fb4mNMz+qv3hgSjstKsZy0BIREWVusFFYTLwqb3U7Kl5uRcerv0Mj7qaUWrsRJcE3oy1m+HBEmcL5VKCRCj4qcS/4qJgfL8iT05QunE+ebF5BTQgO2XHulgpOQnm3y2H3y6rrxSYkybpjIWoCBAJp2ZPqly0siIlB8M+RK/eDj45djZArd8wvzx7I0JQSeHQ/+Kh4IeuBUAjWqVSsoJosuZuYpL67CFhKCVa6F7yE24g4CQmPk9DIOHHC1zkNZIGKj0mWOxkoy2jK19tDBSYFFvBR+6xCkQJSoUh+KV8kvwQVKaB+U3y8XF9iDvv6l/8uyG+7LqnfUEtQchLZkHBsZbdgKyIiIiIiIiKXBiSh1NnUqVPVfdS/GzNmjNn5SuvK6Vy5csViBiU8Z+41pq+3FJCkvT495bJatGhhvH/u3DkGJFGaTFcDnnxKPvvkI1n6+0J5ffzbUqHC/Quy8MO3MyUqKkqVA3x8wEDuQaJ0GvTUEBWQ9M/mTbJr505p1rx5queXLP5dzp87p+4/+dRg7mciExhRn2zjilO7ZtWkSZ0K6v6WPaft3oc+3l4y6YWe6n5UzF3ZtPNkquf3n7h8bx08ZETf1vLVL2nLttWqXEoebFBZ3d956H75NyIiSj9kUVHBRdEpwUUIMkKA0S2TgCP1fNRdiYjLeGk1PVxjR9BASqaj+1mPKhcrqLKgEGUVlGFDxiNMk3oZVIDdhnvBSXsvhKkSZuakZD2KkK83nlGBMIDvS3q/Dzj+65T2l1r3go9w658vc7Li5PXylHKB+dVkSWJSstyISglauh+wFJvqbwQtJSRlQtSSg5DhKi4h5XcLWdnM7V8EoSFISQUrqaCllPsIYMrM3xwMjtkdHCZzt5+XtUdDLB5PKGn3WOOyMrhFkDoWiIiIiIiIiHKrdPfSX3vtNRWEBJ9++qkqdWZJTV0JniNHjki3buZL8OA5cyV46tSpk2oeLM/a62vVYrkssiwsLEySk5LSZHNAhq1bN28aH8/r65sqsO3FMeNk3tw5EnL9ujz+aE/5dvZcadioscTHx6vHP3r/XTXf0GEjVEYlIkqfQYOHyIyvv5QjRw7LE/0fk9k//iztO3RU39VlS5fIC8+PUPOhnBseJ6LUypYIkEXTn5Ufft8qG/47IcFX7mcxKluisAx4uKm8Mfwh1Ta7dSdavv71ftBQq8ZVZPyIh+SXFTtly+7TciX0jnrcy8tDWjWqIh+82Eua1EkJyP34+9USHpU6S8CydfvVPKWK+cuk0T3FN6+3zF68TW6HR0t+Xx/p3rauTBnXR7y9PeVufILMWriFHx8RkQkElUbEJaQEEWkBRiaT9lzK/bsWS1Q5m7dnHqlYtIBUvVdeTQUflSiospYgEITI3QYWVS3hp6bn21aW8JgE+ef0DZU9afPJUAmzkNXHkUAkZOqpWdJPapX2v1d6zV9qlPRzu++Dl6eHlPLPpyZrvz34bUGAUkhEnETHJ0r03SSJvpuo7sfE37uv/k5SgZDa8+o59Xdipgc1IQYIma8w/XsmdbZOrQxeSoBSfqkQmJJdSQtYCsjvna6SkMi89ceBKzJ3+wU5fi3C4nz4fRzcooIKRirky7JsRERERERERHkMGN6TjjJtWmYkBCMhOMmWNm3ayNatW6VDhw6yYcOGNM9jNapUqaKyEw0ePFh+/vnnVM8FBQXJxYsXVUm4OXPmmC2XhUAmZKh59913ZdKkSQ5tE5Y5fPhwdX/Pnj3SuHFjcQbtREd47P0AGHKtutUrycWLF2zON3DQYJn1w0+pHtu/b6/06dVNbt+6ZSwNGBcXJwkJCcZSbb8t/kPy5s2bSWtP6eEO6dzJMReCg6Vr5/bqVivlhoAkfN+gQYOGsurvDRIQEMBdm00ENB3t6lXINcqXCpSTq943/o3An4joOMmX10cK5r///+n85ZvyxKuz5eDJlKxG0LpxVfl79svGv2Ni4yU67q74F8ynsiNBUlKyfP7TOpk4Y4XZ929Su4Is+ep5Y1k2iIiKVe+NICiIjr0rI96dL8vWH3Dy1pM5cQdmqNt0NPspi2l9B35WuQcCAJCVZcvpG7L19A05ExqtMhlZyrqRVVAyKaW0GgKO7gcfIRMJAhuIsjt8xw5cClOZkzaeuGE1yETj5+sltUqlBB1pwUeVixXgd8JEfGJySrCSLoDJGMx0L4gJz0fhNlVw0/3nIu8mqsAovM6Z/PJ6GUu/4Ra/adrfJQv5qkyjepfDYmT+fxdk4e5LVsvSta9eTIY8GCRtqhZLswyi7Iht0uyDnxUREeV0Fy5cUAkl8uXLp2IFiChrBAcHS2xsrLo+W6FCSrWN9LZHHQ5I0gcjIUOStcxI5gJ+sFI7duyQ5iYleBYtWiT9+/dX99evXy8dO6bOevHOO+/Ihx9+qDb66NGjaX50EBj1xhtvqHJZx44dk2rV7meowSZaGwF1+/Ztadq0qQqGKleunNrB2gWzjGJAUs4KSILQkBD5Yuqnsmb1X3Ll8iWVSalWrTryxKCn5Kkhzzjt2CHnYUBS9hQZGSlfTPtc/li2VIKDz6vvVtWq1aTfgCdk1Asvio9PSikFyh4YkJR1vL08pUe7utKmcVVpWjdIZSsqUriAJCUZ5OadKDl06oqs3HxIFq7eI3F3U19YCfQvIE/1ai7N61WU2lVKS5HCBaVQAV+JiYuX4Ku3ZPv+szJnyb9y9MxVq+uA5Yzo10q6ta4jVSsUV8uIvZsg56/clM27Tsms3/5JlbmJMhcDkrIPXlDIHVAWaeupmyoIadvpmyojSVbANfKA/D6qHJV+KlLARwKM91Myi5QpnI8X1SlXuXonVjadDFXZk/ZcCBNvTw+prcqt3QtAKu0v5QLzpSu7DqUPzuXdjIqXC7ei5cKtGLlwO8Z4/+LtmHSX1LN23qBcQD5jsNKVsFhZfzxEZWSyFNzUt0lKWTZkRiLKSdgmzT74WRERUU53+fJlda3Iy8tLJTZhn4woa/rjZ86ckcTERJWcpWzZslkXkPT666/LZ599pu5PmzZNxo4da/eKY4UbNWokhw8fljJlyqgMSAg6QsaLJUuWqGCliIgIVc5t1apVaV4fHh4uNWrUkOvXr6uSbPPmzVNZjFAuC8FOY8aMUfdHjhwpM2fOTPXa+fPny7Jly+TJJ5+U1q1bS/HixdXjiOpau3atCmQ6deqUeuy3334zBkY5AwOSiFyPAUlErseAJCLXYUBS9sELCjnT3cQk2RscpkpFbTl1065MLPa2cYuYCSxKeSyvBBbwvneb8ph/Pm8GGRFRjoHSlhcRqKSClaLV/eBbKbfXIuIksxJDIlvckBYVpE+jslIgb0oGUaKchm3S7IOfFRER5XRhYWEqNgBKlCihqmYwKIko8yB0CN+7kJAQ9TcqlFmrVuPUgCSUS9PSMSFLRbFixWxmUsKkh8xD7du3V7fmSvA0bNhQlXOztFF79+6Vrl27yi0L5bK6dOkif/75Z5pyWXPnzpWnn37a+HeBAgXE19dX7ty5I0lJKemf8RoEWY0aNUqciQFJRK7HgCQi12NAEpHrMCAp++AFhZwBXezzN6Nly6kbsuX0Tfnv3C27yw6V8veVkv6+ukCj+4FFqQOOfCS/jydPwhERmRGXkKTKralgpXsZlbRgpUthMZKQ5Fi0Es6vdqxRQoY+GCQtqxThby/leGyTZh/8rIiIKKdDwpOzZ8+qeAJApiRvb2+2yYky6Zwm4m7wvdNigipXrqy+dxlpj9o9lEf7omv3tagoS6KiotI8hjJrhw4dUqXeli5dKufPn1c/GrVr15YnnnhCXnzRegkeZERCubYpU6bIypUr5dKlSyq4qE6dOjJkyBB55hnz5bIQBPXRRx+pUnHHjx9XAU3IuFSoUCGV3q1Dhw7y3HPPScWKFe3dHURERERERETGTB3bz9xSZdgQiHQ5LNauPYOgogcrF5E21YpJm6rFJIhlf4iIMszX21OqFPdTk6mkZINcC481Bishu9KFmykl4S7eipZoXQBpIV8vGdCsvDz1QAUpF5ifnwwRERERURZDIET58uVV4hTEJyBQQguWIKLMg5gbfPesBSPZy6GSbZSOHXwvKiw81r4RsUTkfMyQROR6zJBE5DrMkJR9cIRz9pGcbJDDV8LvZUG6Ifsu3lEXue1Ru3QhYwBS4woBbKsSEbkJnCK9FR0vF25FS2KSQeqW9Zf8PizLRrkP26TZBz8rIiLKLRCEFBkZKdHR0cbqR0TkfJ6eniohECqV2ROM5NQMSURERERERES5VWhEnPxzrwzbttM3JCwmpXS4LUUL+kjrqsWkTbWi0qpKMSnml7rEOBERuQecSC1aMK+aiIiIiIjIfSAwIiAgQE1ElL0wIImIiIiIiIjIxN3EJNl9PsxYhu3E9Ui79pG3Zx6V+UjLglSrVCHx8EgZLURERERERJTdhUTEyfdbzsnt6Hh5snl5aRIU6OpVIiIiIjfFgCQiIiIiIiLK9ZBa+OyNaGMZtv/O3ZK4hGS79ktQkfzGAKQHKheRgnnZ1SYiIiIiopzXZ1q674pMWnFUIuIS1WPLD1yRIS2C5LWu1aUA+0FERERkgmdJiYiIiIiIKNcKi46XX3dekN92X5LLYbF2vaaAj6c8WKWoCkJqW7WYlC+SP9PXk4iIiIiIyJVZkSYsPSwbToSmetxgEJm7PVg2nAiRKX3qqX4SERERkYYBSURERERERLnYjci7MmvzWRnYvJxUKe4nucXFWzEyZ9s5WbTnssQmJNmcv24Zf2lTrajKgtSoQoB4e3pkyXoSERERERG5U1Ykcy7djpWBs3fKE83Ky/iHa0ghX+8sXU8iIiJyTwxIIiIiIiIiysUW770sP/57Xk3NggLlieblpFudUuLr7Sk50f6LYfLD1nOy5sh1STZYnq9owbzSpmpRaVu9mLSqUlSKFMyblatJRERERETk8qxI45celo0mWZGgXll/qVbCT/Un9RbsuiibT4bK5D51pX314lm4tkREROSO8hgQ3kyZt4Pz5FG34bG2R9wSUebw8eLodSJXC2g62tWrQJRrxR2YoW7Z7M8+fYes/KySkw3S7vPNcvF2TKrH/fN5y2ONyuaYrEnYzvXHQ1Qg0u7gMIvzNQ0KkA41SqhMSDVLFhIPj5TPhIiIiCi3cEWblNKHnxVlFnz/l+y7Iu+byYrk4+khL3eqKs+1qSRenh6y7fRNeXPpIbPlr/s0KiPv9qglhfP78MMiIiLKpe1RBiRl0YfAgCQi12FAEpHrMSCJyHUYkJR9uOKCwrGrEdLzm22SZCVVUHbOmhSXkCRL9l2W2VvPy/mb0Wbn8fTIIz3rlZLhrStJnTL+Wb6ORERERO6EQS7ZBz8rygzXw+NkwjLLWZE+61tfqpdMPWgl+m6ifLrmhPy840Ka1xTzyysfPlJHutYuyQ+MiIgoh2FAkhtgQBKR6zEgicj1GJBE5DoMSMo+XHVBAWn4f99zSRbsuiRX7qQd1Zodsybdiror8/+7IPN2XJDb0fFm5ymY10ueaFZOnm5ZUUoXzpfl60hERETkjhjkkn3wsyJnQj8U5dfeX3lMIm1kRbJk57lb8saSQxJ8K3UGXuhRr5RM6lWbpbCJiIhyEAYkuQEGJBG5HgOSiFyPAUlErsOApOzD1RcUkCVp6+kb8r+dF2XDidBsmTXp3I0omb3tvCzZe1nuJiabnaeUv68807Ki9G9WTgr5emf5OhIRERG5M1e3Scl+/KzImVmRxi89JJtO3kjzXH1kRepXX6qVsG9QSmx8kkxff0pmbz0npl3KwAI+MrFXbZWhVjt+iYiIKPtiQJIbYEASkesxIInI9RiQROQ6DEjKPtzpgkJ2ypqE/bXnQph8v+WcrD8eIpZ2X61SheTZNpWke71S4m1lVC8RERFRbuZObVKyjp8VZXZWpDGdq8qzra1nRbJk/8UweW3xITkTGpXmuc61SshHj9SR4oV8M7T+RERE5FoMSHIDDEgicj0GJBG5HgOSiFyHAUnZhzteUHDnrElYl7VHr6tApAOX7licr221YioQ6cHKRTgKl4iIiCgbtknJPH5WlJlZkT7vV1+q2pkVyZK7iUny9YYzMuufs2n6koV8veSdHrWkb+Oy7KcRERFlUwxIcgMMSCJyPQYkEbkeA5KIXIcBSdmHu19QcJesSTHxifL7nssyZ9t5uXg7xuw83p55pHeDMjK8dUWpUbKQ09eBiIiIKKdy9zYp3cfPitID3+3f916WDyxkRRrbuZqMaF0xXVmRLDlyJVxlSzp+LcLsAJLJfepKmcL5nPZ+RERElDUYkOQGGJBE5HoMSCJyPQYkEbkOA5Kyj+xyQUHLmrRg10VZfzzrsiaFRsbJvO0XZP5/FyQ8NsHsPBhl++QDFWTog0FSgun/iYiIiHJsm5T4WZHjroXHyvilh2VzJmZFsiQhKVlmbT4rX288LQlJqX9fCub1kvEP15AnmpYXD4+U3yAiIiJyfwxIcgMMSCJyPQYkEbkeA5KIXIcBSdlHdrz4kxVZk06HRMrsredl2f4rEp+UbHYejKYd1qqiPN60nDqZTURERES5p02aW/GzIoeyIu25lxXpbtZkRbLk5PVIeX3xQTl4OTzNcy0qFZEpj9WT8kXyZ/p6EBERUcYxIMkNMCCJyPUYkETkegxIInIdBiRlH9n5goKzsyZhH/x37rb8sPWcbDwRanFZ9cr6y7NtKslDtUtmyclzIiIiopwuO7dJcxt+VmRvVqQ3lxyWf06ZyYpUrrB83rdepmVFsiQxKVmV4J667pTEJ6YedJLP21Nef6i6DGkRxGxJREREbo4BSW6AAUlErseAJCLXY0ASkeswICn7yCkXFDKSNQknplcduS4/bDknh6+kHTGr6VSzuIxoXUmaVQw07jciIiIiyric0ibNDfhZUbqzInl5yCudq8nwVlmTFcmSszei5I3Fh2TPhbA0zzWpECBT+taTysUKumTdiIiIyDYGJLkBBiQRuR4DkohcjwFJRK7DgKTsI6ddUHA0a1LzSoGydN8Vi0FMaNM91qiMDGtVSaoU50lpIiIiosyQ09qkORk/K7Lk6p1YGb/Uclakqf3qOVxKO7OgnzhvR7B8uuakxCYkuWXgFBEREZnHgCQ3wIAkItdjQBKR6zEgich1GJCUfeTkCwr2Zk0yp3B+bxn8QAV5qkWQFPPLm2nrSEREREQ5u02a0/CzIlP43i7ac0k+XHncbFakcZ2ryTA3De65eCtG3lhySHacu2W2VPdnfetL9ZLuEURFREREKRiQ5AYYkETkegxIInI9BiQRuQ4DkrKP3HBBwZGsSRWK5FcjYR9rXFby+3hl6XoSERER5Va5oU2aU/CzItOsSG8uPSxbzGRFalCusHzuRlmRLMHvDgaxTF51XKJMAqq8PfPIix2qysh2lcXbDQOqiIiIciN72qP8r01ERERERNlWZGSkTJw4UerWrSsFCxYUf39/adq0qUydOlXi4+MztOzFixdLz549pXTp0uLj4yMFChSQ6tWry4gRI+TAgQNO24bcxNMjj7SrXly+e6qJbH+zg7zapZqUKZwv1TyNyheWbwc1ko3j2qmsSAxGIiIiIiJnYN+BciJcAPxt10XpMn1LmmAkDNQd362GLBn5oNsHI2kXNQc2Ly9rx7aRttWKpXouIckg09adkl7f/CtHroS7bB2JiIjIMXkMHOqQqZghicj1mCGJyPWYIYnIdXJyhqQLFy5Iu3btJDg4WP2dP39+SUpKkrt376q/GzZsKBs2bJCAgACHlovX9+vXT1asWGF8DMFOCHDSgpw8PDzk888/l7Fjxzpte3LrCGdkSfr3zE05ejVCmlUMlMYVHPu8iIiIiMh5cmqbNKf1HXLyZ0X2QznsN5cckq2nb6Z5rmH5wqrMWZXiBbPlLsVxvWTfFXl/xVGJiEtMM9Dl+baVVMYkX29Pl60jERFRbpeHGZKIiIiIiCgnSkxMVNmLcEGhVKlSsm7dOomOjpaYmBj57bffxM/PT/bv3y+DBg1yeNmTJ082XlAYNWqUXL58WY2mjo2NlT179kirVq0kOTlZxo0bJ3v37s2ErctdcDK5TbViKvU+g5GIiIiIyNnYd6CcmhWp6/QtaYKRMDh3wsM1ZPHzD2bbYCTtAmffxmVl/SttpXOtEmkGtczYdFZ6fL1NNp8MlYSkZJetJxEREVnHDEmZjBmSiFyPGZKIXI8ZkohcJ6dmSJozZ44MHz5c3d++fbu0aNEi1fMLFiyQgQMHqvvr16+Xjh072r3sihUrqkCntm3byubNm9M8Hx4eLmXLlpWoqCh588035eOPPxZn4AhnIiIiInK1nNgmzYl9h5z6WZFtyckGeWXRAVl+4GqOy4pkCY7xlYeuyXt/HpXb0WlLs/vn85ZONUtI19ol1GAXZk0iotwgPjFZdgffVoGZ4bEJUq9sYRXAWaKQr6tXjXKRPHa0R72ycH2IiIiIiIic4ueff1a37du3T3NBAQYMGCBvvfWWnD9/XubNm+fQRYVr166p2yZNmph93t/fX6pVqyb79u1TFxaIiIiIiMh9se9AOcn09afSBCPl9fKQV7tUl2daVVQZaHPixc6e9UvLg5WLqKAkBCfp4UL8kn2X1ZTP21PaVS8mD9UpKe1rFJdCvt4uW29yLlzsjolPkqi7iRIZlyCRcYnqfjG/vFK9hJ/xojhRToagTAQgbTgeKltO3ZDIu/dLWi7ac1neXn5E6pf1ly61S6rgpKrFC/K7QS7HgCQiIiIiIspWUJbt33//Vfe7detmdh6ciHrooYdk1qxZ8vfffzu0/EqVKsnx48ctlmPDKOdTp05ZDVoiIiIiIiLXY9+BcpI/DlyRrzeeSfVYI2RF6ldfKhfLWVmRzClSMK98M7CR9Kh3Xd7544jciLybZp7YhCRZfeS6mrw980iLykXloXsX5hG4kt1dDotRGVF2nU+Zrt6Jk3w+npLfx1MK+HhJ/rz3bvF3XpNbHy91v0BezO8lBfC6vCa3Pl6ZUnECZfWi4hBIlCgRcQn3gooQUJQSWHR/uv+cFnSkBR5hQrk+c+qW8ZcX2leWLrVKikcODMqj3B2Idzo0StYfD5GNx0Nl38UwsfA1MDp4OVxNn609KRWK5JfONUuo38AmQYE5MmiV3B8DkoiIiIiIKFtBsFBycrK6X6dOHYvzac9dv35dbt++LYGBgXYtf+TIkfLSSy+pkgsvvPCCTJgwQcqUKaNOAuzfv19efvlllRkJmZkGDRrkpK0iIiIiIiJnY9+BcooDl+7Ia4sPpXqsU83i8t1TTXLdBWZkP2pdtaisOxYia45cl39O3VCBSKYSkgwqgwimt5YfliYVAqRr7ZJqKheYX9wdzkGcvRGtAo+0IKQrd2LTzIdtvx3tvPdFIFeagCUVzGRy6+Mpvj6eEhefpLK0qMAhBBDdTdAFH6UEHcUlpJzDySyHr4TL87/sk8rFCsjzbSvLIw3LiLen8wOriLLC3cQk9X1HFqQNJ0Lk0u2033t7XbgVI7O3nVdTQH5v6VCjhHSpXUL9huJ7TJQV8hhYYDhzd/C9FIHhsWkbQ0SUNTIjop+IHBPQdDR3GZGLxB2YoW5zUrN/xYoV0qtXL3X/4MGDUq9ePbPz/fHHH/LII4+o+4cPH7YavKSHYKfx48fL559/bgx8KliwoMTHx6upZMmSMnToUHn33XclX758dq+3venDc9JnRURERETZi9ZmzSlt0uzadwD2H0hzPTxOen2zTUJ1GYFQomrJqAelYF5eUI6NT5Itp2/I2qPXZf2xEBUEY0utUoVUYBOCk6qVcI+SRsj+c/xahDH7EYKQbkXHu3q1sqXS/r7ybJtK0r9peZVBisjd3Yq6K5tO3pANx0NUEGV0vO24Aj9fL2lXvbh0rFFcygbkk00nQ+XvoyEqo5ItKPWJoCRkTupYs4QULZj9M8iR+/Yd2FIhIiIiIqJsJTIy0ng/f37Loxr1z+lfY4uHh4d8/PHHUqtWLRk9erTKhoRJExcXp8q2RUdHO3xRgYiIiIiIsg77DpQTgm1GzNuTKhgpsICPzB7ShMFI9yDgRMt8hNJgO8/dljVHr6kL8/r9pnfsWoSapq07JRWLFlAZQ1DarX7ZwllW8gtZUA5fDped94KP9gaHqUxD9vLyyKNK9SUmJ0tMfJJE301UQQyWypplN7jGXdDHSwVdFPTFrXfK/bwp9wv5eomXZx7548BVuRyWOoPM1fA4mbjimCpx+HTLIHmqRZD45/N22bYQmULwxsmQyJQsSMdDZP+lO2JPLDh+rxCAhCCiJkEBqTKBoSTba11rSPDNaJVBDtOeC7fNlni7m5gs64+HqilPnsPSqHyACk7ClBtKgFLWYkASERERERGRzs2bN6Vfv36qZFvnzp3lvffeUyOkY2NjZceOHfLGG2/IrFmzZO3atbJlyxZVzs0etkaZu8OITCIiIiIicn3fAdh/IBwDry4+qMpR6ctpfTuocbYoOeYKuDjfqmpRNb3fq466yP/30euy5uh1VbrInPM3o+W7f86pqUShvMbgpmYVA51a9gsBQ/sv3pFd52+pICSU4UNQgL18vT2kYbkAtV7NKwZKg/KF05RcwjETn5QsMXeTJDo+0RiolOo2PjEleOluksTgfnySxNwLZlJ/a4/rno93YD21qhUIGELgUEoA0f1AItzXpoJ5U/+tn7+Aj5ddwWFjO1WTFYeuyqzNZ+VUSOrMMMgw9fnfp+Tbf87JoAcqyDOtgqS4n69D20LkzCDE/86hFFuICkQyV4LRFEpyNg0KkI41kMmouFSyI1goqGgBGdGmkpqQeWnjiVD5+1iIbD19w2z5RJyu3HshTE2frD6hSh92rlVSBSc1LJd1QZqUc7FkW2bvYJZsI3I5lmwjcj2WbCNyHZZsc7zsQvfu3WXVqlXStm1b2bRpU5pAodDQUKldu7a6+DBo0CCZP3++Uz6rnFYeg4iIiIiyn5zWJs2pfYec+FlRWl9tOK0y+OhNeayuKkNF6ctGsvZIiApOQmk0Wwrn91ZBACjthtJGvt6Olf66ExMvu4PDVADSruAwOXIl3KHsRQjKaRoUqCYEIdUt4++yax3IPBVjJmApNiFR7ZdC+sAjXy/J65X1ZdKSkw2y4USozNh0RgV7mYP993iTsvJcm8oM6qMscSPyrmw6ESobTiAg6Kb6HtmCbF7tqhdTWZDaVi0m/vm9nZZxb9uZm7Lu2HUVEGVPSUiUcutUs7gKTmpZxfHfQcr57GmPMiApiz6E8FjbPzBElDkYkETkegxIInKdnBiQtHfvXmnSpIm6j5P/3bp1MzvfzJkz5YUXXlD3b926JYGBgTaXffz4cVWqDf7880/p2bOn2fnGjRsn06ZNU2XhUM7NGdmNeEGBiIiIiFwtp7VJc2rfISd+VpTa6sPXZOSv+1I9NqxVRXmnR8oxRxlz8VaMrD16XU17L4bZLJWU38dTBQggc1L7GsVVAI6pkIg42XX+tnFCAJQjcOG/WcUAaaYCkIpI9ZJ+KjsKOQa/iTvO3VIZkxAAYg72a6/6pWVku8pSrYQfdzE59fg7fg2l2EJk/YlQOWghOM4UshIhAAnl2BpXCBAvJ2ZnMwfBkfsvhqmybsiehExxtuTz9pS21Yqp4KQONYpLQAGfTF1Hyh7saY+yZBsREREREWUrNWvWFA8PD0lOTpYjR45YvKiA56BkyZJ2XVCAY8eOGe9XrlzZ4nxVq1ZVtzExMWrUc4kSJRzcCiIiIiIiymzsO1B2hEw6YxcdSPUYLgKP71bDZeuU05Qvkt9Y0ig0Mk5dlF9z5LrsOHtLEs1kMUJWk1WHr6sJZfMerFxUBSd5eeaR3QhACr5tsSScJWUD8qnMRykBSIFSsWgBlnJ30sVxfD6YDl2+owKTkBVLf60cwRjL9l9RU6eaJWRU+8rSqHyAM96ecqG4hCT124EsSBuPh8rV8Dibr/HyyKO+91oQEsqsZSUE5TUJClTTm91qyNkbUSowCb+FKC1pTmxCkvouYVKvrxCggpO61CqpflOJLGFAEhERERERZSsYWdyyZUvZunWrrFmzRl577bU082BUxtq1a9X9Ll262L1sBDppLly4YBzxbCokJMR4v2BB2/XbiYiIiIgo67HvQNlNaEScjJi3R+ISklNlzvh6YMNMz5iRWxX3+z979wEeRbm2cfwmnUAIvffeFZBepKkgoNgAFRt2saLi5/HY6wHLOfaCBbsoWFCkiogCKr0LhF5CSSCE9MJ3vTOkLCkksMls+f+ua66dzMzuzs6mzJu593nCdHXXBtYUl5imX/7Zb7V2+3XTAZf3IUtaxnEt2HTQmoqjWfXy6tyosro2stuw1a5Y1o2vAvlpX7ei3hrdSVsOHNPbC6L03Yo9eQJnc00lmw371b1xFSuY1KtpVYJhyP7f4tHkdOv38v6jKYq2bnOm6KMp1roD8SlFasdYKTxY/VpUV/9W1dWnebV8K605FeJrWj3Cmu7o29QKaZqWbiacZFq8pabn/T1oXu+f22Kt6ZmfNqhFjQi1rxupmpFhqlEhTDXNFBmm6hVCVbVcqAKo9ubXaNlW0geYlm2A42jZBjiPlm2Ac3yxZZvx/vvv66abbrLOtxcvXqyuXbu6rJ8yZYpGjhxpzc+dO1cDBgwo0uOaEFLDhg2tedNywbReOFlCQoLat2+vrVu3WrerVq1yy2ui5QIAAACc5ovnpL44dvDV98rfmSobo95dopW5WvxElg3W92N7lnr1DEhJqRn6bfNBq63b3PX7rWBCUZlr721qR1oVUEz4qHPDSqpSPpTD6rA9R5L03m9b9eXfO/MNmxnt6kTqjr5NrApYhCh8+/ftgaMp2h+frOg4O2BkgkVZ8/aUYlUFOhMmiGhVQWpV3arC5W1tGBNS0vXbpoNWOGnexgOKS0or9mOYalDVI0JVI9IOKpnAkhVaigzNDi+Z23KhQR77vXIkMU2xCak6kpiq2MRUHU5M0+EEc5t64jZNKekZalY9QmfXq6iz6lVU46rl/OJ3SJkinI8SSCqlNyEu6cx+YQE4fQSSAOcRSAKc46uBpPT0dHXs2FFr1qxRnTp1NHnyZOvCgWnjNnXqVOuCw9GjR612bjNmzHC57xNPPKEnn3zSmt+2bVv2RYQsF110kaZPn27Njx492tq+cePG1nP+/fffuueee7R06VJrvXnea6+91i2viQsKAAAAcJovnpP64tjBV98rf2bex3FTVlktpHJfxP14TBf1aFrV0X2DqYqUqT+3xmrmun2avW6/FVzILSQwwLoI3blRJXVpVEUd61dUhIdUP0FeMcdS9OEf2zV58XbFFxA0M5XJbju3iYZ3qKNgqpN5DVO5x7y/djUj+9ZUMbKCRvF2RSOzzARMSoJp6di1URUrgDSgZQ2famWWnpGpv7cftsJJs9dHa/fhJLc+fkRo0EmhpdA8FZeqlg89o1CXCZqaEJEdLkqzwkVWyCghJ1hkhYysoJE9b1p2nu7raV8v0g4o1a1o3VavECZfQyDJAxBIApxHIAlwHoEkwDm+Gkgytm/frn79+lm3We0YzEWF5GS7V3uHDh00b948VapUqVgXFQ4dOqRBgwZp2bJl2cvMY6emploXFrKYVnETJkxw2+vhggIAAACc5qvnpL42dvDl98pfvfnrFk2Y+Y/LsmeGt9Xobg0c2yfkLzPzuFbsOqIlW2Nkfgw71a9kVcMICw7kkHmZ+OQ0ffbnTk1auE2HjrmGzLLUjgzTzX0aa1Tn+iobwnvsaQEZE+I07cX2xSVZQaOD8SkqQvc0t8iq/GNCJk2rl1f/ltXVu1lVvwgjmnOPjdHxVjjJTGv2xJXK85osUrWI0OzQUlZgyUwmDGYFi3JXL8oVLDJTQZXRSkvtyDDr74U11a1otbnz1MpQRUUgyQMQSAKcRyAJcB6BJMA5vhxIMuLj4/Xiiy9q2rRp1gWCgIAANW/eXFdeeaXuuusuhYSE5LnPqS4qGObigfkE89dff62VK1cqNjZWgYGBql27tnr06KFbb71VvXr1cutr4YICAAAAnObL56S+NHbw9ffK38xeF61bP12m3G/ltd0b6KmL2zq5W4DfMC2Zvl62W+8siCqw6kvlciEa07Ohrune0GqlCOeYv3uz1+/XhJkbFXUwoUSew7zf2VV6KoRZoSM7BGO3GTNTlXIhftGSqyiOpaQrOi7JrkoVZ1ehMm3vclrgpehAfHKphcW8RUAZ09YvQmfVi7RCSqaKUosaEQryoqpsBJI8AIEkwHkEkgDnEUgCnOPrgSRfwgUFAAAAOI1zUu/Be+UbNuw7qsveWuTSEqZn0yr66IYutIkCHKi48+PqfVbFsk37j+W7TfnQIF3drb5u7NVI1SN8r/2Sp/trW6xe+HmDlu88clr3LxcSaLUFqxFxIlx0Yt6utGOHjUwFntAgqmGVRDs9U4ksK7CU1T4vOi7FDi+dCDEV1EaxNIUGBViBs4rhIVY4rWJ48InbEFUOD1alciGqdNI6E7ZavfuIVu2K06pdR7Ry1xHrNRVXWHCA2tbOCSiZqW6lstnnfZ6GQJIHIJAEOI9AEuA8AkmAcwgkeQ8uKAAAAMBpnJN6D94r72cuzF78+h/acySnIkujquX03R09FRlOBRbAybZ88zYesIJJKwoIvpjrTiPOqatb+zRRvcrhpb6P/uaf6HirIpJ5X/Jj2nWZgFjuCkZ5KhxFhlmBMni2hJR0l4BSVtWl7GVxyToQn6L0IpZbCg8JtMJDlcoF27f5hoxyvjbr3dWe0ez3KiukZAeUVu+Os6pJFZfZr7PqRurseqZFaKTV7s2EojwBgSQPQCAJcB6BJMB5BJIA5xBI8h5cUAAAAIDTOCf1HrxX3i0lPUOjJ/2pv7cfzl4WERakb+/oqabVyzu6bwCUXW18ydZYK5i0cPOhfA9LYEAZta5VQa1qRaiVdVtBrWpWIFToJiaw+cqcTZq6fLdLW8vcIc4Hzm+hwW1r0j7Nz0KDMQmp2W3hTFDJ/LxmVS7KHToKCw70qP3eeuiYVu6K08pdh61qSqZSYlHDVbk1qBJuVU8y4SRTTalN7QqOvFYCSR6AQBLgPAJJgPMIJAHOIZDkPbigAAAAAKdxTuo9eK+8l7loOv6b1fp62e7sZQFlZLVp69O8mqP7BiB/phXTW79Gaea66HyDMSerHRmWE1CqVUEta0WoYZVyVoAJp3Y4IdUKgk1evEOp6Zl51lctH6p7BzbTyM71aG8Jr5aclqH1+45q5c4j2dWUtsckFvtxggLKWL9rujWurEeGtJYnnY9SlwwAAAAAAAAAAAAoBe//vs0ljGQ8OrQ1YSTAg7WvW1Fvje6kLQeO6e0FUfpuxZ5Cq5rsjUu2ptwtxsoGB6p5zQi1PlFNqWVNO6hUIYwWjVmSUjP04aJtVvgrPjlvayvTcu22cxtrTK9GCg8h5gDvFxYcqI71K1lT7kCeHU6Ks25Nu7fYhNRCH8f8PlqzJ876PeNpyhwvLK4Et6XC4pIyOJqAQ6iQBDiPCkmAc6iQ5D34hDMAAACcxjmp9+C98k7zNx7QjZP/Vu4cw5Vd6um5S9plv6cAvKOV2Ae/b9OCTQe19eAxl5/p4qpbqWyudm92WKl+5XC/akGWnpGpb5bt1itzN2n/0ZQ864MDy+iabg01tl8TVSkf6sg+Ak45fvy4dh9OsoJJpoKSuTXho5R8qofd3LuRx1VIIpBUSm8CgSTAOQSSAOcRSAKcQyDJe3BBAQAAAE7jnNR78F55n83743Xpm4sUn5JT9aNro8r65Mau/A8b8PKWS5v2x2vjvnir9dKGE9PRfCr8FFW5kEC1OBFOalmrglVVqUXNClaFIF9iQgyz1u3XxFkbFXUwIc96c5l9+Nl1NO685qpXOdyRfQQ8UVpGpvV7x1RRWrnrsHW76UC8Xruyg4a2r11q+0EgyQMQSAKcRyAJcB6BJMA5BJK8BxcUAAAA4DTOSb0H75V3Me1Xhr/5h3bEJGYvq1e5rL4f20uVy4U4um8ASiZoY1q2bdh7VBujTUAp3gopbYtJ0Jn0LmpQJVwtTwSVzNS6VgWrwpI3Vlj7c2uMXpi5USt2Hsl3/bnNq2n8oBZqUzuy1PcN8EbHUtIVFFDGagNXWggkeQACSYDzCCQBziOQBDiHQJL34IICAAAAnMY5qffgvfKuKgbXvP+nlmyNzV5mqpxMu6OHmteIcHTfAJSuxNR0/RMdr43RdkDJTKayUu7KacVVKTxYnRtWVpdG9mRCSkGBAfJU5vVPmLlR8zYeyHf9WXUj9dDglurRpGqp7xsA95+P+lZdNwAAAAAAAAAAAMADmAt0j/+wziWMZK7dvXrl2YSRAD8UHhKkDvUrWVPu3xO7DyedCCidCCpFH3WpqFaYw4lpmr1+vzVltXvrZAJKDSupS6Mqal83slQrphRkz5EkvTx7k6at2J1vlahGVcvpwQtaaHDbml5Z8QlA/ggkAQAAAAAAAAAAAG728eId+vzPnS7LHh7cUv1b1uBYA7CY8E29yuHWdH6bmi7tl/7J1e7NqqYUHa/E1IxCj1xCaoZ+23TQmrK6iJxdt2J2BaWODSpZVdpKs2Xlm79u0eTFO5SanplnfbWIUN07sJlGnFNPwR5c2QnA6SlzvLD6SThjtGwDnEfLNsB5tGwDnEPLNu9BywUAAAA4jXNS78F75fkWbj6o6z/8WxmZOZfhLutYVy9e0Z7qHwBOS2bmce2MTdTG6KNavy9e6/bEaemOw4pLSivyYwQGlFGb2hXUpWFldW5U2Wr3VrlciNvfkaTUDH3wxza9vSBK8cl5W9KZUNRt5zbWmF6NrMpRAHzzfJRAUim9CXFJhadVAZQcAkmA8wgkAc4hkOQ9uKAAAPB7qQnSis+k0PJS64ulkHJ+f0gAzknB+ME7bT14TMPf+ENHc12E79Sgkj6/uatCg5xvnQTAt0JKmw7E669tsdnTgfiUYj1G8xrlrWCSqaDUtVEV1YwMO+39Sc/I1NfLduu/czdp/9G8+xESGKDR3Rrozv5NSyQIBaD0EEjyAASSAOcRSAKcRyAJcA6BJO9BIAkA4NcSDkkfDZUObrC/rtRIGv6m1KCH03sG+BXOSb0H75XniktM0yVv/qGthxKyl9WpWFbfje1ptSYCgJJkKpXsiEnUX9tzAkqmqlJx1KtcVl0aVlFXU0GpUWU1rBJ+yspu5nlnrduvCbM2auvBnN9/WczdLzm7ju47r7nVng6A9yOQ5AEIJAHOI5AEOI9AEuAcAknegwsKAAC/lRAjTR4mHVh30ooyUrfbpf6PSiFctABKA+ek3oP3yjOZyiA3fPS3Fm4+lL0sPCRQ39zWQ61rV3B03wD4r+i45BMBpRj9ve2w/tkfX6z7mzClXT3JbvHWokaEAgJyAkp/bo3RCzM3asXOI/nev2+Lahp/QUt+DwI+hkCSByCQBDiPQBLgPAJJgHMIJHkPLigAAPxSYqw0+SJp/5qCt6ncxK6WVL9bae4Z4Jc4J/UevFee6Ykf1umjRdtdlr1zTSdd0KamY/sEACc7nJCqv7fHWpOpoLR271FlZB4v8oGKLBuszg0r6ZyGla37/7LxQL7bnVWvov5vUEt1b1KFNwHwQQSSPOhNiI5LdXpXAL81cUGU07sA+L3rO9T1+2MAOKVdvYjsssnwbFxQAAD4naTDdhgperXr8vCqUmJOZQlbGan7WKn/v6XgsqW5l4Bf4ZzUe/BeeZ7P/tyhR75d67LswQtaaGy/po7tEwAURUJKupbvPGyFi/7cFquVu44oNT3ztA9e46rlrN9/g9rWPGWrNwC+fT4aVIr7AwAAAAAAAABS0hHp4+F5w0gXvii1HyHN+pe04tNcK45Li1+XNs2Uhr8l1evCUQQAeIzFUTF6/HvX1qMXn11bd/Rt4tg+AUBRlQsNUu9m1azJSEnP0OrdcVZAyUzLdhzWsZT0IrV2u3dgM404p56CAwN4AwCozHE+Kl2iqJAEOI8KSYDzqJAEOIcKSd6DTzgDAPxGcpwdRtq73HX54AlS11tzvt40W5p+txS/z3W7MgF2taR+j1AtCXAzzkm9B++V59gRk6CL3/hDRxLTXNoUfXVLN4UFBzq6bwDgDukZmdoYHW9VT/prW4z+3n5YsQk53YEiQoN0W98muqFnQ4WHUA8F8BdlilAhiUBSKb0JtGwDnEMgCXAegSTAOQSSvAcXFAAAfiH5qPTppdLuv12XX/C81P2O/CspmWpJKz/Lu65qc+niN6V6nUtufwE/wzmp9+C98gzxyWm65M1F2nLgWPaymhXC9MOdPVW9Qpij+wYAJcWED6IOHrOCSWkZmRravrYqlwvhgAN+pkwRAknUSgMAAAAAAABQ8lLipc8uzxtGOv+Z/MNIRtmK0vA3paumSOVruq47tEn64HxpzmNSWnLJ7TfghPhojjvg4TIyj+vuL1a4hJHCggP03rXnEEYC4PMhhKbVI3Rll/q6tntDwkgACkQgCQAAAAAAAEDJSjkmfXaFtOtP1+UDn5B63HXq+ze/QBq7RDrrStflxzOlP/4nvdNH2r3Mvfvsb8ynWtdOk97rL73VU1owQYrb4/Re+Z+0JGn+c9J/20ub5zq9NwAK8cLPGzT/n4Muy1664my1qxvJcQMAAKBlW8mjZRvgPFq2Ac6jZRvgHFq2eQ9aLgAAfFZqgh1G2vGH6/L+j0p9Hij+4/3zszT9HunYftflZQKknvdIfR+WgkLPbJ/9zbbfpDmPS3uX5z2mzc6XOl5n3wYGObWH/mHTLGnGg9KRHfbXlRpJdyyRgkuv7RPnpN6D98pZU5bu0vhvVrssu2dAM913XnPH9gkAAKA00bINAAAAAAAAgHNSE6XPR+YNI/V75PTCSEaLwXZIo92IvNWSfn9Feudcac9JwRrkL3qt9Onl0uRhecNIWcd000zpyyulV9pI856SDm/naLrb4R3SF1dJn4/ICSNZy7dJi17jeAMe5u/tsXrk2zUuyy5sV9MKJAEAACBHmePHTS1elBQqJAHOo0IS4DwqJAHOoUKS9+ATzgAAn2w99cUoaeuvrsvPfUjq9y/3PMfGn6Tp90oJB1yXlwmUet1rPxfVkvI6skua/6y06kuTOir+cW/c166a1HIIx/dMpKdIi16VfntJSk/Ku77VRdKg56XIuiotnJN6D96r0pOWkanVu49o0ZYYLd4ao6U7Dis1PTN7fds6FfT1rT1UNiSwFPcKAADA889HCSSV0psQHZda0k8FoAAEkgDnEUgC/DeQlJCQoLvuuss6L37//fcd2QdvwQUFAIBPSUu2q+pE/eK6vPcDUv9/mz987nuuxFi7zdXab/Kuq95aGv6WVPts9z2fNzPH6veXpT/flTJS8q6PqGWHxep2llZ8Kq36QkqMKfjxwqtIZ11ph5Oq0aaoWMzPhvm+jdmSd13lxtKFE6WmA+Vv56SMH7znvfJlGZnHtX7vUS2KOmQFkP7aFqvE1Ix8t60WEaof7uypWpFlS30/AQAAnEQgyQMQSAKcRyAJcB6BJMB/A0kxMTGqVq2adV6ckZH/P3Bh44ICAMCnqr58eZW0Za7r8p73SgOfcG8YKbf1P0g/jZMSDuatltR7nNRnvBQUIr+tVvXnO3YYKTku7/rQClKv+6Sut0kh4a7v5T8zpGWTpa3zC3+O+t3tYFLri10fA67i9kiz/iWt/y7vkQkKk3rfL/W4WwoO88tzUsYP3vNe+RJzDDftP2YHkKJitGRrjI4mp5/yfqFBAfrylm7qUL9SqewnAACAJyGQ5AEIJAHOI5AEOI9AEuAcAknegwsKAACfYAIsX10jbZ7lurzHXdJ5T5dcGClLQow04wFp3bS862q0lYa/KdU6S34jM8OucjT/OenonrzrA0OkzjdLfR6QwisX/liHt0vLP5FWfibF7yt4u9BIqf0IqdN1Us12Z/4afEVGmrTkLenXF6S0hLzrmw+SBv9HqtRQ/nxOSiDJe94rb2aO2faYRCuAtCgqRn9ujdGhY0XvclGnYln1aFJFt57bRE2rly/RfQUAAPBUBJI8AIEkwHkEkgDnEUgCnEMgyXtwQQEA4PXSU6Up10qbfnZd3m2sdMGzJR9Gym3dd3a1pJPbjQUE2W3jTBUaX66WZAIKm2dLc5+QDqzPZ4Mydmio3yNSpQbFe+yMdPuxl39sB8+OZxa8be0OdtWkdpdLoXblTr+0/XfppwekgxvyrqtYXxo8QWoxWJ7A6XNSAkne8155mz1HkrRoi10BybRh2xeXXOT7mrZs3RtXsUJIPZpUVb3KZbOPPwAAgL8qU4Tz0aBS3B8AAAAAXuipp5467fsmJia6dV8AAIAHV3/55oa8YSTTAqy0w0hGm+FSw17ST/e7tsbKTJcWvCBt/Em65C3frOCze6k053Fpx+/5r2/SXxr4pFSr/ek9fmCQ1PJCezq6V1rxmbTiY+nIzrzb7l1hT7MekdpeKnW6XqrTqfS/H5wSv1+a86i0+qv8q1P1vEfqNc7nWtwxfoAnOBCfbIePTgSQdsQUfWxaMTxY3RpVUY+mdgipSbXyBJAAAABOQ5njxOdLFBWSAOdRIQlwHhWSAO+ukBQQEHBG/3w1z23un5GRcdqP4Q/4hDMAwLvDSGOkDT+4LjetwC6c6Hz4ZO00O5iUFJu3WlKf8VLvcVJgsLxeTJQ070lp/ff5rzet6kwQqUk/9z93Zqa0db60fLK0cYaUmVbwttXb2O3cTIWmspXkk0wVqaXvS788I6UczT8UNniiVLWpfPGclPFD6WD84OpwQqr+3BZjtWAzIaTNB44V+ViWDw1Sl0aVrfBR9yZV1KpmBQUE+ElwEgAA4DTRss0DEEgCnEcgCXAegSTANwJJNWrUUGhoaLHum5mZqV27dhFIKgIuKAAAvDZ4MfVG1ypExjljpCEvOx9GynLsoN3C7eTQlFGzvTTcVEtqK6907ID06wt2GMhUgDpZxQbSgMekNpeaE7tS2J+D0qov7P2J2VLwdoGhUuuL7XBSg56e871ypnb9ZX+vRa/Juy6itjToeft1e+jrdWcgifFDyfL38UN8cpr+3h6rRVvsENKG6KNWt8qiCAsO0DkNKlvhIxNCalcnUkGBpfD7EQAAwIcQSPIABJIA5xFIApxHIAnw7kBSw4YNrVDRF198oREjRhTrvocOHVL16tUJJBWBv19QAAB4aRjp21uktVNdl3e8Vhr6v9IJvxSH+Rtr9nXGA1LSYdd1AcHSuQ9Jve71nmpJKfHSotekRa9LaQl514dXsStAnXODFFS8ULnbjveORXYwyVRtSk8ueNsqTe3vm7OukspXk1dKiJHmPiat+DTvOlONq9sd9vdYaHn5+jkp44fS4a/jh12xifr3d2v1+5ZDysgs2msPDiyjDvUqZQeQzq5fUaFBgSW+rwAAAP5+PhpUivsDAAAAwAt16tTJCiQtX7682IGkM2n1BgBAqbcdi/rFDlHUaCNF1vXYCiYeITND+u72vGGkDqM9M4xkmPez3eVSw952BZuNP+asMy3G5j9jLzPVkmq0lkd/ry77SFrwHynhYN71weFS97FSj7ulsApy9Hg37GlPg/8jrZ4iLZssHViXd1tTSWnOY9K8p6WWF9rhpEZ9pUAv+Pe1aVe3/CNp7pNS8pG86xv0koa8KFVvJX/B+AEl5Whymq6e9Kd2xiYWup3pttaubkUrfGSmTg0qKTzEC36fAAAA+BjOwAAAAAAUqmPHjvr222+tQBIAAD4pPVX69FJp+8KcZWGRUo229mRaeZmQUvXWUnBZJ/fUc8JI34+V1kxxXW6q2wx7zTPDSLlF1JBGfiqt+caulpQ7RLJvpfTuuVK9rlKlhlLlxrmmRlKoXX3SESYst+5b6ZenpditedeXCZQ6XiP1fViKqCmPUraS1PVWqcst0p7ldoBnzdS8lZ1MMMxUUzKTCVbV7ijV6yzVNVMXz6uetHeF9OM4aW8+58nlqksXPCu1u8Lvwo2MH1ASzCfv/zVtTYFhpFa1KmQHkDo3qqwKYV5S7Q4AAMCHEUgCAAAAcMpPOBunE0gKDAxU/fr1FeDpFyYBAP7NVGbJHUYykuOkHX/YU5YyAVLlJicCSrnCShXq+E/gwFSD+eEuadUXrsvbj5Quft3zw0hZzPvV/gqpUW/px/ukf2bkrMtItb8fTv6eMMpVs8NJlRrlDSuZ0E1JfR9sW2h/n+YXfDFaDpUGPC5Vay6PZo5P3U72dMFzdoUtUzUpv9eVlijt+N2espiQWFY4yQSVzM+gEy32TMs/U81p6QcmJuG6zvyeMMGrfv+yg41+iPEDSsKUpbv04+p9Lsuu6lpfvZtWVdfGVVS5XAgHHgAAwMOUOe5vDYZLWVaLiui4VKd3BfBbExdEOb0LgN+7vkNdvz8GgFPa1bM/xX8mp/1JSUn666+/rPk+ffrQhs3BntsAgBJgKs58ff2ZPUZYRddKSmbetGfytWpKJoz04z3S8o9dl7e9XLr0XSkgUF7J/O017cR+ftAOop0uEz7JE1Y6MV++xumFlaLXSnOfkLbMyX99vW7SeU9J9bvKq0WvsYNJ5n1IKcZ7EFRWqt3BtYqSqYBVkt8rJow3+1Ep8VDe9eb5h7wk1Wovfz4nZfxQOvxp/LB5f7yGvf67ktMys5fddm4T/d/glo7uFwAAgD8rU4TzUQJJpfQmEEgCnEMgCXAegSTAuwNJKB3+dEEBADzGoc3Su32l1GM5yxr2lkLKSfvXSXG7Tv+xTZWUKk1zAko129nz3lpNyYSRfhonLfvQdXmbS6RLJ0mBPlCI/eg+ux3a5jlSwgH3PrZpP2aFlRrmDSuZ74mTw1xHdknznztRiSqfc4OqLaSBT0gtBnvn91NB0pKk9T9Im2dLu/+Sjuws/mNUrH9SFaV2UpAbKqeYcJhp8bdzcd514VWkgU9KZ1/tPVXCCsA5qffwl/cqOS1DF7/+h/7ZH5+97Ox6FfX1bd0VHOjdP28AAADejECSByCQBDiPQBLgPAJJgHMIJHkPf7mgAAAeIzVBem+AdHBDzrLqbaSb5koh4TltmUwwyUymgou5PbBBSk9yUzWlExWVPL2akvnb9NP90tL3XZe3uki6/ANnWmaVtJRj0uHtUuxWezq87cT8Nilud/4hodMVGCJVbJATVMpMt6tQZaTk3TailtT3YTv44gshsFOJ3y/t/tsOJ+36W9q7ovg/f0FhUq2zXasoVahV9PsnH5V+fV768x3peMZJK8tIna6XBjwmhVeWL+Cc1Hv4y3v1r2/X6PM/c8KJEWFBmnF3b9WrfOJvNQAAABxBIMkDEEgCnEcgCXAegSTAOQSSvIe/XFAAAI9gftd+e5u0+sucZSER0i2/SlWbFn7fzAw7mGIFlNaeCCutlY6akMqZVlPK1fKtRmspsp7z1W/Msfr5Iemvd1yXtxwqXfGRb4aRTiU9RTq846Sg0omw0pEddqDI3UIrSL3ulbrenhOY80cZafbPnQknZQWVTHCsuMzPVt1zTlRR6mJXMAsKzfu9v3aqNOsR6Vh03scwIaehL0t1OsmXcE7qPfzhvfpp9T6N/Xy5y7I3ruqoIe2LESoEAABAiSCQ5AEIJAHOI5AEOI9AEuDdgaSnnnpK5cuX17hx49y4Z/DHCwoA4DGWfij9eK/rsismS22Gn/5jZlVTMuEkK6i09kQ1peQzC6FUb22Hk6xKSias1FoKi1SpMH+TZv1LWvKm6/IWF9rHyx1tsHxNRrrd6i93RaWsW7OsuN8PpnpS55ul3vdL5aqU1F57t2MHpN1Lc1VRWi6lJRbvMQJDpVpn2eEkE1SKqC3Nf0ba9lvebc3Pn6mI1OmGvK32fIA7zkkZP5QOXx8/7IpN1IWvLlR8ck7I86qu9fXcJe0c3S8AAADYCCR5AAJJgPMIJAHOI5AEeHcgKSAgQDVr1tTevXvzrLvxxht15MgRTZ069Yz2E75/QQEAPIZp+fT++VJGas6ybndIg553/3OZakoxUdL+NbnCSuvOrJqSUaHuiUpKrU+ElNpIVZu5t1qR+Xs0+9/S4tddlze7QBr5Sd5qMji1zEwpfl/eqkpZt6nxrtu3GyH1/7dUqQFHt7ihsAPrpF1/naii9Ld9jN3BtMob+KRUvprPvifuOCdl/FA6fHn8kJaRqSveXqyVu45kL2tRI0Lf39lTYcG+FwQEAADwRgSSPACBJMB5BJIA5xFIAnw3kFSrVi0dOHBAGRkZZ7Sf8O0LCgDgMUwVo3f6SEd25iwzLZuu/6l0q/0kxtrBJGs6EVY602pKAcFS1eZ5g0oVahe/7Zv5WzT3cemP/7kub3qeNOozwkglwRzzxBg7OGMq/pj3rnKjEnkqv5RwKCecZIJKe0wVpYSi39+0URzyklS/m3xdSQeSGD+4jy+PH57/eYPeWZATJAwLDtD0O3upWQ17fAsAAADvOB8NKsX9AQAAAAAAAJyrTvPtba5hpPAq0hUflX7rsfDKUqPe9pS7qktsVE5Q6cB6+/bIjqI9ZmaaXRXGTGtOai+VFU7KCipVbyWFVcj/ccw/En95Om8YqUl/aeSnhJFKivlHbrmq9gT3M8e1xWB7yvp5O7jBtYpSzJa89wuJkPo/YrfNC+Rf6UBpWLDpoEsYyXjyojaEkQAAALwQoygAAAAAAAD4vj/+K22amWtBGemySVJkHXkEE3ao1sKe2l6aszwl3q6edHJQKTmnjU2hkuOknYvsKbeK9U8ElVrbYSUzX6WptOA/0sKXXLdt3Fca9bkUHOaGFwp4yM9bzXb21PnGnMplWeGk6DVSZD2pzwNSRE2n9xbwGweOJmvcVytdlg07q7ZGnFPPsX0CAADA6SOQBAAAAAAAAN+2baFd9Se3fv+yq/54utAIqV4Xe8pdxejo3pxwUlZQ6eA/dqWkojCVosy06WfXtm8n379RH2nUF1JwWTe9IMBDmcplzS+wJwClLjPzuO6bslIxCanZy+pXDtezl7TNbgcCAAAA70IgCQAAAAAAAL4rPlr6Zox0PDNnWZMBUu8H5LXMhVlT2clMzc7LWZ6RJh3anDeoFLeraI97chipQS/pyi+lkHD37j8AACd5a0GU/tgSk/11UEAZvXZlB1UIC+ZYAQAAeCkCSQAAAAAAAPBNGenS1zdICQdyllWoK136nhQQIJ8TGHyiBVtrqd3lOcuTjtht3w6YkNL6nKBSytGCH6t+D+mqr6SQcqWy6wAA/7VsR6xenrPJZdlDg1rqrHoVHdsnAAAAnDkCSQAAAABO6dixY3rqqafyXW7kt+5kjz32GEcaAFC6fnlK2rnItSXZiMlSuSr+9U6UrSg16G5Pudu+xe0+EU7KFVSK32u3srvoNSm0vJN7DcCLMX5AUR1JTNXdX6xURubx7GX9WlTTjb0acRABAAC8XJnjx81/H1BiB/hEb+PouJy+xwBK18QFURxywGHXd6jr9C4AfqtdvQjr9kxO+wMCArLPa89ERkbGGT+GL8s6xgzRAMBNNv4kfXmV67LBE6Wut3CIAaAEz0kZP5QOXxg/mH2/7dNlmrVuf/ay6hGh+vme3qpSPtTRfQMAAMCZn49SIQkAAADAKXnzP7kBAH4odqv07e2uy9peJnW52ak9AgC/wvgBRfHpkh0uYSRzTeu/o84mjAQAAOAjCCQBAAAAKFRmZiZHCADgPdKSpCnXSilxOcuqNpeG/c++0gkAKFGMH1AU6/ce1dM/bXBZdle/purRpCoHEAAAwEcQSAIAAAAAAO6RmWGHQdKTi39rpqAwqXx1qXyNE9OJ+eCyvEMoup/HS9Frcr4ODpdGfCyF2m1MAQCAsxJT03XnF8uVmp7z4ZfODSvp7gHNHN0vAAAAuBeBJAAAAAAA/FlqohS9WjqwQUo9JqWZcFBSMW9PBIsy00pmH0Mr5Aoqmdua+QeXylWVAgJLZh/gHVZ8Ji3/2HXZsFel6q2c2iMAAHCSx75fp60HE7K/rhgerP+N6qCgwACOFQAAgA8hkAQAAADALZYsWaLU1FT16dOHIwp4qvRU6cA6ac9yae9yac8K6eAG6biHt2ZMOWpPMVsK365MgBRe1TWkFHFSaClr3oScaN/lW0xVpJ/GuS4750ap/RVO7REAoBCMH/zTtyt265tlu12WTbz8LNWuSEVMAAAAX0MgCQAAAIBbXHLJJTp48KDS09M5ooCntE87+I+0d8WJ8NFyaf9aKSNVPssEqxIO2NP+U2ybX3u4ig2ktpdKFeuX0g7DbZLjpCnX2tW6stTuIA16noMMAB6K8YP/2XYoQf/+dq3Lsut7NNR5rWs4tk8AAAAoOQSSAAAAALjN8ePHOZqAE8zPXuzWE+GjFXb4aN8qKS2nFYb7lJGCy9qBngJvw6SgssW/TUuUjh2QjkVLx/afmM91mxjjvpdhgitHdtpTbr++IPX7l9TtDimQf5t4zff/92Ptn4EsYRWlKyZLQaFO7hkA4BQYP/iPlPQM3fXFciWkZmQva1O7gh6+sKWj+wUAAICSw3/WAAAAAADwtvDF0b121aOs8JG5TT5S/McKCJKqt5bqdJRqnW1XCSo0aFRWCgxxrtVZRpqUcFCKj84bVjp53oSbTkd6kjTnUWntVOmi16Ra7d39KuBuS96UNkx3XXbpu1KlBhxrAAA8xH9+/kdr9xzN/jo8JFCvXdlBoUGBju4XAAAASg6BJAAAAAAAPFlCTE7Ltaz2ayZwU2xlpKrN7fBRbTN1kGq2tUNG3iIwWKpQ255OFdpKPVZwWCk+1zLT3s20ejvZvpXSu32lnndL5z7kXcfJn+xcIs15zHVZ7wek5hc4tUcAAOAkc9fv1wd/bHNZ9szwtmpcrTzHCgAAwIcRSAIAAAAAwFMkH7WDMLnDRye3FCuqig1cw0e1zpLCKsgvmApOoRH2VKVJ4dtmZkiJsfZxnv+MFPVLzrrjGdLvr0jrf5AuelVq2KvEdx3FcOyg9PX1UmZ6zrJGfeyWewAAwCPsi0vSA9+scll2acc6urRjXcf2CQAAAKWDQBIAAAAAtzhuKpIAKFx6ihS/z265Zk17cuYPbpQObTY/TcU/iuVruoaPzFSuCu9GUQQESuWr2dPoadLqr6SZ/yclHc7ZJjZK+miI1Ol6aeCTUtmKHFunmSDZ1Bvtn6csEbWkyz6w31MAgMdj/OD70jMydc8XK3UkMS17WeOq5fT0xW0d3S8AAACUDgJJAAAAANzi1VdfVVJSEkcT/ivl2ImwUVbIyNxmhY9OLEs8dObPE1bRNXxk5k/VwgxFr6x01iipyQA7lLT2G9f1yz6S/pkpDXlJajWUo+qkX5+Xti3I+bpMoHT5h3awDADgFRg/+L7Xftmiv7bHZn8dEhig167qoHKhXJoCAADwB2WO8zGEkj3A5p+ZkqLjUkv2iQAUaOKCKI4O4LDrO1CGG3BKu3oR1i2n/d4zduC98kCm+lfykfyrGuWeUuLc/9zB5aTaZ+dUPTLho0qN7OAMSp4JH/00zn7PT9b6YmnwRCmiBu9Eads8R/rsctdl5z8j9biL9wIAzhDnpN7D09+rxVExunrSEmXm2r0nL2qj63o0dHK3AAAAUIrno8TQAQAAAACIibJbprmEjHIFj9JLofpXYIhUs51r5aOqzWk/5aQWg6QGPaR5T0l/T3Jtp7f+e2nrr9L5z0odRhMSKy1HdkrTbnZd1nKo1P3OUtsFAABQuNiEVN371QqXMNJ5rWvo2u4NOHQAAAB+hEASAAAAgDOybt06LV26VAcOHLC+rl69ujp37qzWrVtzZOH5ko5IP9wpbZhess8TGGq3VatQR6pQK9e8ua0tRdSWylcnfOSJwipIQ16U2l0u/XCXdGhTzrrkOPv7Z80Uadj/pMqNndxT35eeIk25Tko6nLPMVAwb/iaBMADwIowffJv5hPwDX6/S/qMp2ctqR4Zp4uXtsz9FDwAAAP9AIAkAAADAaZk1a5bGjx+vtWvX5ru+Xbt2mjBhgs4//3yOMDzTvtXSlGukw9vP7HFCyruGi7KnOjlho/DKBCa8Xf1u0m2/S7+9KP3+spSZnrNu22/Smz2kfg9L3cZKgV7475a0JGnnYikjTarXVSpbUR5n1r+kvctzvg4Kk0Z+IoVFOrlXAIAiYvzgH97/fZt+2Wh/WMUIDCijV6/soIrhIY7uFwAAAEpfmeOe2mDYR2Ql/qPjUp3eFcBvTVwQ5fQuAH7v+g51/f4YAE5pVy/CunX3af/rr7+ue++913pcMwUGBqpq1arWupiYGKWnp2efD7/66qsaO3asW5/fX3tuw41WfCb9NE5KTy58u7KVC69qZG5NBR34l/3rpB/ulvYszbuu1lnSRa9LtdrL46UmSlvmSOu+kzbNktIS7OUBQVLD3lKroVKLIfb3v9NWfy1Nu8l12cVv2O3yAAAef07K+ME/xg+rdx/RZW8tUlpGzj49cH5z3dm/maP7BQAAAGfORwkklTACSYDzCCQBziOQBPhWIGnVqlXq1KmTMjMz1bVrVz3++OPq16+fQkNDrfUpKSmaP3++nn76aS1evNgKKy1btkzt23vBxXkHeeIFBZ+Uliz9PF5aPvmkFWWkLrdIdc+RIrLCR7Wl4LIO7Sg8XmaG9Ne70rynpLRE13VlAqWed0vnPuR530OpCdLm2XYIydyevO/5qdtZajlEajlMqtpUpe7ARum9/jmBKcMEkUwgCQDg8eekjB/8Y/wQn5ymoa/9rh0xOecWPZpU0Sc3drWqJAEAAMC3EEjyAASSAOcRSAKcRyAJ8K1A0nXXXadPPvlEw4YN07Rp06zAUX4yMjJ06aWXavr06dZ9PvzwQ7ftgy/ytAsKPsm0ZptyrbRvlevyspWkS9+Tmp3n1J7Bmx3eIf14rxT1S951lZtIF70qNewlR6UckzbPOhFCmiOlJ53+Y1VreSKcNFSq3aHkWxGafTdhpEP/5Cyr0U66aY7nhb0AwAeUxDkp4wffHz+Yfbjny5X6YdXe7GVVyoVoxj29VaNCmKP7BgAAgJJBIMkDEEgCnEcgCXAegSTAtwJJDRs21K5duxQVFWXNF2bbtm1q0qSJ6tevr+3bt7ttH3yRJ11Q8EmbZkvTbpaSj7gur3W2NOJjqVIDp/YMvsD83K7+Spr5f1LS4bzrO10vDXxSKlux9PYpJd5uw7buW2nL3FO3JwyJkFoMlkIjpH9mSPH7Tv0cpoVhVjipQU8pMMhtu599XKfeKK2dmrMstIJ0y69SlSbufS4AQImdkzJ+8P3xw5S/d2n81NUuyz68obP6taju2D4BAACgZBFI8gAEkgDnEUgCnEcgCfCtQFLZsmWtKTY2tkjbV65cWUlJSdYED7ygYFqYfXa51PlGqfXwkq924kRbrV9fkH6bkHddpxukQS9IwXxqG25y7KAdSlr7Td515WtKQ16UWg0rucOdfFTaNNOuhGRCSBkphW9vwj0tLpTaDJea9JeC7NabysyU9q6QNk6XNvwoxWw+9XObSmPNB9sBJfNYIeFn/nr+ek+a8YDrspGfluwxBAA/VxLnpIwffDuQtOVAvNWqLTktM3vZLX0a618XtnJ0vwAAAFCyCCR5AAJJniU2NkazZvyo3xf8otWrVmj3rp3KSE9XlarVdFaHjhpx5TW6cNjwfO9bMzKkyM/To/e5mvbjHDfuOc4EgaTSlZaSpN1r/9b+Leu0P2q99ketU/xB+9PV3UeNVY+r7sz3fnH792jSzQOL/DxtBlyiQfc857Is4fBB7V631H7eLet0YOt6JcfHWetGPDtZ9dp1OaPXhtNHIMkzHYs/qq8+maT5s3/Szm1ROnYsXpUqV1WDRk10TrdeGn3jHaoQmVPJ4bspn+rR+28/5eO++/kP6t67XwnvPZwMJJmAUWJioo4dO6agoMKrYaSnp6t8+fIKDw8vcoDJXzl2QeH3V6S5T9jz9bpK5z8r1essn5AQY1dX2TrfdXlQmDT0Fensq5zaM/g6U5nox3HS0d1517W6SLrwRSmihnueKzlO+udnO4QUNU/KSC18+7BIqcUQO4TUuG9OCKkwB/+RNv5oh5P2Lj/19kFlpaYD7MpJzS+Qwiur2HYvkz64QMpMy1nW4y7p/GeK/1gAAEfPSRk/+G4gKTktQ8Pf+EMbo+Ozl51Vr6K+vrW7QoICHNsvAAAAeMb5qJtraQOerX2zetZFsSxhYWEKCg7Wvr17rGnmT9PV/7xBmvTxl9ZFs9yqVS/8n8XpaWk6fNi+yHZ2h04l9AoAzxe9aY2mPXlrse9XJiBA4RWrFrpNRlqKUhLsf3DUbNY2z/pVP3+lxV++UeznBvzRX4t+0/g7b1DMwQPW18EhIQorG64D0Xut6e/FC9X/gqEugaQsAQEBqlSl4J/XkJCih3jhnVq1aqUlS5bom2++0ahRowrd9uuvv1Zqaqo6deL8yGOrufz2Us7Xu/6U3h8otblUGviEd7cx271UmnJd3kBIpUbSyE+kmu2c2jP4AxPCGbtEmvuk9Pck86+ZnHUbfpC2LbCDNR2uOb2qZElH7LZqVgjpF9fQTn7CKtrhIBNCanSuFFTMv9XVWthT7/uluD32c2+YLm3/XTqekXf79CQ7wGSmMoFSw152VSNTjSmyzqmfLzFW+vo619dVv7s04PHi7TcAwCMwfvBdz/y03iWMFBEapNdGdSCMBAAAgOIFkmJiYvTDDz9o3rx5Wr58uXbs2GEFO6pVq6ZzzjlH1113nS655JJCHyM+Pl4vvfSSpk6dqm3btikwMFDNmze3LmLcddddp7x4tX//fk2YMEE//vijdu7caZV6bdOmjfXcN954Y3YCKz9mXydPnqwvv/xSq1at0uHDh60wiulf3b9/f919991q0qRJUQ8HvJT5PujQqbNGXnWt+g04Tw0aNbaW79yxXf+d+Lw+/+RD/TJnpsbfe4def/cjl/uu2byr0Md+67VX9OS/H7Lmr7r2hhJ8FYDnCysfqepNWql649aq0aS1fn3/BSUcPlTofSpUq6XbP15Y6Dbz3nlGK3/6TEEhYWrZZ2jeDcqUUUTVWqrexH7ecpWrac7rj53pywF8zoq/F2vsdZcrOTlJAwdfpJvG3q/W7TtY51JJSYmK+meDfpn9k8pHVMj3/jVr19WsxetKfb/hOa644gotXrxYd9xxhzUeGDBgQL7bzZ0719rGfG+NGDGi1PcTRXAsWqpQWzr0j+vyddOkjT9J3W6zAwimooq3MJ/IMQGQmQ/nDWmYqjDD35TK5g1bAm4XGmG3aGt3ufTD3a4/Z6aq0Q93SWu+lob+V6rSpGghnawQ0tZfTx1CMi3UcoeQAoPlFiZQ1OVmezL7tHm2HU7aMs8OIp3MBJZMAMtMpv1a7Y5Sq6H2vpmQ08lMu7hpt0hxucbg5apJl3/ovtcAAChVjB98089r9unTJTtdlj13aTvVr+KGtq0AAADwCWWOF7GeZ3BwcJ7KMiZQlJCQkL1s8ODB1qekT64sY5gAU9++fbV9+3bra7NNRkaGUlJSrK87dOhghZ0qVaqU7/MvW7ZMF1xwgRWMMkzbh+Tk5Ox9MutMYCq/UJMJH5l9+/PPP7OXRUREKCkpKfv+oaGh+uSTT6zBkTvRss2z/P7br+rVp2+B68ffO1Yff/ieNb9sXZTq1K1X5Mfu3aW9Nv+zUV2799T3M09qCQFH0bKtdGVmZCggMNBl2Xs3DdDRA3sLbdl2KumpKXrn+nOVfCxOrfoO04XjJpzyuXO3gaNlm7No2eY5TODo0oHdtHvnNl11w216+KmJRb5vVsu22nXrE0jy85Zt5hzefChh3bp11vlu9+7dNXDgQNWpY1e92L17t3Vub0JL5nnbtm2rpUuXUj3LU1suZKRLyydL85+TEvMJEIdXkfo+LHW63vPDAKkJ0vR7pTVTXJeXCbArq/S85/Sq0QBnKj1FWviStPDlvEEi00Kw37+kbmOlwJM+N2YCP6bKkAkhmUBPZs7/ZfJlfl6zQkgNe5fuz2xqol2tyYQZN/0sJR0+9X2qNDsRThom1e5gyjBKCyZK859x/fm99nupUZ8S3X0AQMmdkzJ+8L2WbbtiE3XhqwsVn5xzbnJll3p6/tL2pb4vAAAA8Nzz0SI38TXBnS5duujNN99UVFSUFeY5duyYVenIVCcyfv75Z91666353nfYsGFWGKlWrVqaM2eOFWRKTEy0KhaZcNCKFSs0evTofJ87Li5OQ4cOtcJILVu21N9//21VWzKP8frrr1thqVmzZunee+/N9/5meVYY6YknntChQ4d09OhRK9D066+/WlWWzKDIVFras2dPUQ8JvFBhYSTjymtyKhutWrGsyI/795+LrTCSQXUk+LuTw0jusnnxHCuMZLQ7//JSfW7Al0yf+qUVRqpavYbG/etpp3cHXsqE+c35txkfmMHGokWL9PTTT+u2226zpmeeecZaZtZ17dpVM2fOJIzkyUwAovON0t0rpF73SYGhrusTY+yqJm92l/752a5A5IkObZbeG5A3jFSuunTtD1KvewkjwTlBoXbo6NbfpDrnuK5LT5bmPCZN6i/tWy0lxEjLPpI+Hi5NbGpXUoqaV3AYKbyq1OkGO7Rz/ybpolelJv1LP0AYEm6Hiy55S3pgi/1z1+VWqUIhLdpiNku/v2K/9lfaSNNuleY/67pNv0cIIwGAl2P84FvSMjJ195crXMJIzaqX12ND2zi6XwAAAPA8RQ4k/fLLL1ao5/bbb1fjxnabK8O0PJs0aVJ2EOnTTz/Vrl2ura1Mq7Q1a9ZY86Zdm/n0tPXkAQEaOXKk3nnnHevrGTNmWJ+kPtmLL76o6Ohoq0Wb2cZ8Gtsw1ZDGjh2rJ5980vr63Xff1aZNm1zua4JGX331lTVvAkePP/64qlSpYn1tKjyde+65+v77762vTcjKtIOD/woLy7n4Yip4FdXnH39o3VaIjNSw4fkHJQCcmbVzplq3lWo3UL22XTicwGmaPvVz6/b8IZcoNCyM44jTVrt2bSt0ZD5gYFo3161b1zo/N5OZN8vMefgff/xhbQsvEFZBGviEdNdSqd0V+QcHvhglTR4m7Vslj2Kqx7zbTzq4wXV5/e52AKRRb6f2DHBVo7V042xp0AtScDnXdebn6t2+0ovNpOn3SFvn2+3O8mOCdufcKF03XXpgkzTsv1LjvnkrLDnF7Efjc6ULJ0j3rZNunm+3f6zWsuD7xO+VVn9pPleXs6zZBVKvcaWyywCAksX4wXe8PGeTVuw8kv11aFCAXr+qo8qG8EFBAAAAuCryf6r69etX6HpTJSkrWGTaMdSrV88lkJT1GKadw8lGjRqlRx55xKq29PHHH2vAgAEu682yrO0aNWqU5/533XWXnnvuOati02effZYdUMpq15bVFi4ryHSyJk2aqHLlyoqNjbUeA/7rj99/y55v1aZtke6TcOyYfvjuG2t++GUj821ZCODMHInepZ1r7Ep3bc+7jMMJnKbUlBStW73Cmm/d7mzt27NL77w6Qb/Pn6OYQwdUIbKi2p3dSSNG36g+AwYV+DiHYw5pxIW9tT1qs9UqsWqNmjq7U1ddduV16tydi/7+xHzAYMSIEdYEH1KxvnTZJKnr7dKsf0m7lriu375Qeudc6eyrpP7/lio4GDjLSJPmPiEtfj3vuu532gErT28zB/8TECh1u11qcaH043129aMsBQWQjPI1pdYXSa0vtsN25nG8gSnfXaejPQ14TDq0Rdo43W7ttvvvwn8XXfK23cYNAOATGD94v982HdRbv0a5LHviojZqUdNuFQ4AAADk5rb/6oTl+oR97soypi2b+VS0MXjw4AJ7yw0aZF/0mj17tsu6f/75Rzt37iz0/uXLl1fv3r3zvX+NGjVUrly57KBUfkwLOhNGKiy0BN8Xd+SIXnt5gjXfrUcvNW3Wokj3+27qFCuUZFx97ZgS3UfAX62dO9VqDxMQGKQ2/Yc7vTuA19qze4fSUlOt+d07t+uSgV019fOPFBtzUGXDyyn20EEtmDtTY6+/Qk+Mv7PAvr9JSYnasGalgoNDlHk8U3t2btdP336lMSMu1KP3326164XvX0gICgrSli1bnN4VlKS6naQxM6URH0uVTv5gyHFp5WfSa52k+c9JKQ58sOPoPrta08lhpJDy0hWTpQueJYwEz1apgTR6qnTJu1LZyvlvE1FL6nqbdMPP0rgN0oUTpYa9vCeMlJ+qTe32kDfNlcZtlIa8ZLeYC8j1mbmgMPvnOLyA4wIA8CqMH3zH5EXbXb4e0r6WRnXO+XA6AAAAUCKBpF9//TV7vl27dtnzGzZsUGZmpjXftm3BFWey1pnWbFnhIGPt2rV5tins/uvXr88TdspqJ2cqNZnqSTExMdnBqQULFujiiy+2vr7iiiusFm7wP+Z79M5br9f+6H1WuO65if8t8n0/+/gD67ZNu/Y6q0PHEtxLwD+Z6ivr5n1nzTc651yVq1TN6V0CvNbRuJyS6u++OsEKlLz09if6c2O0Fq3dpdlL1uv8oZdY66d+MVkfv+d6kb96jVq6/b6H9c3sxVq25ZD+WLtTf286oE++naNuve1qmt9N+VQTnvy/Un5lKG2mlbL5UEDTpk05+L7OVDYx1VjG/iVd8JwUVtF1fVqitOA/djBp+cfmD3fp7Nf236V3+kg7F7suN+2gbvlVakOAGV70M3bWSPtnrP0oO5RToa5doWzMLOm+9dLg/0gNevhmpaAKtaTON0nXfCs9GCVdOsmuonTLAruiEgDAJzB+8B1vje6kW/o0tubrViqr5y9tZ12DAQAAAPLjlv9mHTlyRM8//7w1byoVtWiRU1lm79692fN16tQp8DFyr8t9n+Le/+jRo3narj377LO69tprrfknnnhCVatWVWRkpBU86du3r5KSkvSf//xHX3zxRZFfM3zLvx8apzkzZ1jzz734qlq3bV+k+23csE7Ll/5lzV9FdSSgRGxfvlDHYvZb8+3Op10bcCaOnwiJZ4Vxn5z4hs4fMlzBwXY7o1p16mniGx+pRWs7XD7p9Rddqh31OHeA7hj3L7Vo1VYhoaHWssDAQJ19Tje98+l36nf+EGvZVx+/px3bqJzjy+rWrau0tDSndwOlKShE6j5WunuF1O0O10omxrFo6Ye77JBQ1PyS2w9Tue2P/0mTL5ISDriua3u5dNM8qWqzknt+oKSUryZd+o70yH5p3Dpp8AtS/W6+GUIqSNmKUvsrpN73S9VbOr03AAA3YvzgO0KCAvSvC1vpwxs66/WrOqpCGO2RAQAAULAz/s+WuZh1zTXXaN8+u7LM66+7fpI+Pj4+ez48PLzAx8m9Lvd9zvT+htmvSZMmaeLEidkX3ExwKesCm2krZ6oypaSkqLhM+r+wCZ7viUce0gfvvmnNP/X8i7rqmuuLfN/PP/4w+3vs8hFXldg+Av5szZxvrNvyVWqoUcc+Tu8O4NXCy0Vkzzdo1EQDBg3Lt5T+dbfebc0fORyr9WtWFOmxzf0e+Pez2eeHv8752W37Dc8zZMgQJScnW9VG4WdM+6RBz9vVXFoOzbt+/1rpk+HSZ1dIBza697mT46SvRktzHpOO56rEFBAsXfiidNkkKbS8e58TKG2BJ4X9AADwAYwffE+/FtV1dr2TqqcCAAAA7g4k3XPPPfrxxx+t+TfeeEPt2xetskxp2rZtmzp16qQHH3xQl112mZYuXWqFlnbu3KmPPvrICg6ZCkl9+vTJU10Jvu2pR/9Pb7/+ijX/+DP/0S132BdgiyI1NVXffPW5NT/koksUWZEBGOBuCYcPaevf9sXuNgOGKyAwkIMMnIEaNWtlzzdq0rzA7Zo0y6lKsHf3riI/fv1GTVSpchVrfvfO7ae9n/B8Dz/8sKpVq6bbb7/d+mAC/FCVJtKoz6TrZ0i1O+Rdv3m29FYP6cf7pGMHz/z5otdK7/aVNtpjz2wV6kg3/Cx1udlufQUAAACPw/gBAAAA8E9n9NG7Bx54ILsi0iuvvKIxY8bk2SYiIueT+KYSUUFyr8t9n5PvX6FChWLdPyMjQxdffLHWrFljtW2bPHly9rry5cvruuuuU+fOndWxY0ctW7bMCiY9/fTTKqrjpmVAIaiS5Lme/Pf/6a3XXrbmH33qed1+133Fuv/Mn35QbMwha552bUDJWD//e2VmpFsXGNsOpF0bcKYiK1VW9Zq1dSA6pyXuqc5vOJdBfjZs2GC1Rb7vvvvUunVrq2Jqz549Vb16dauNX0HMBwDgYxr2lG76RVr7jTT3Seno7px1porR0g+k1V9Lve+zW70Fly3+c6z6Upp+r5Se5Lq8cV/psvelclXP/HUAAACgxDB+AAAAAPzTaQeSxo8fr5deesmaf/HFF3Xvvffmu13t2rWz5/fs2VNgBSWzLr/7nHz/ggJJWfc3603QKMvs2bOtMFJWgCo/5iKKKRs7bdo0TZ06tViBJHhvm7asykgmjDT2nvuL/RhZ7doaNW6qHr24uAaUhDVzplq39dt1VcWa9TjIgBv06NNf3035VFu3bCpwm62bc9os1a3XoMiPvWv7Vh2OjSn2/eB9+vbt6xJWM5VSzVQYs31Wy2T4mIAAqf0IqdUwacmb0sJXpNRcbbTN/LynpKUfSgMel9peZt/nVNJTpJn/Z4eaTtbnQanvw1IA1RMBAAA8HeMHAAAAwD+dVss20/ps4sSJ1vyECRN0//0FhzlatWqlgBP/bF67dm2B22Wtq1mzpipXrpy9vG3btnm2Kez+JlyU2/r167PnmzRpUuD9mzVrlt3eDf4TRjJt2k4njLR710799us8a/7K0ddRPQIoAbvXL9PhPfbv5LbnXc4xBtxk+IjR1u3O7VGaN3N6nvWZmZn66J1XrXlTTalVu7OLVBXSrH/p2X9b8+bcr8/AQbxnPs6858WZzPcWfJypftT7funu5VKnG6QyJw0343ZJ026SJg2Qdiwu/LGO7JQ+uCBvGCksUrpqitT/34SRAAAAvAjjBwAAAMD/FDuQZKoMmYpIWWEkE04qTHh4uNW+wZg5c2aBg5FZs2ZZ8+eff77LuubNm6t+/fqF3j8hIUELFy7M9/5ZYShjx44dBe7n/v3787R7g+956tH/yw4jPfncxGK3acvyxacfWRfVgoKCNPLqa928l4D3Sz4Wp8Sjh7On45l2kCEtJclleWpSQoGPsXb2N9ZtWESkmvU4r0jPezwz0+XxzX5kSUmId1mXnpZ6xq8T8EaduvbUeUOGW/NPjL9Tc2Z8n121Zt+eXRo/9gZt2mAHve8e/1j2udTe3Tt15dC+mvLpB9q1Y1t2QMn8PVy1/C/dfs2l2QGnK64eo0ZNmjv0ClEazPt+OlNJiI+P1xNPPKF27dpZlVIjIyOtlsymmmtq6pn/ro+Ojtajjz6qTp06WR+cKFu2rBo0aKBBgwbphRdeUFpamlteh08pX10a9l/p9kVS03z+hu9dLn04SPpqtBQTlXf95rnSO32kvStcl9dsL936m9T8gpLbdwAAAPjs+IGxAwAAAFC6yhw/1cfdTwoj5W7TVlhlpNzef/993XTTTVYVmcWLF6tr164u66dMmaKRI0da83PnztWAAQNc1psLAM8884wVblq3bp0aNmzost4Eox566CEFBgZaFZFMiCnLggULrJKwxl133aVXX7U/8X/yRYaWLVsqLi5OF110kb7//nu5S1Yri+g4Lnw7zVQ1OqdtU2veXFytUrVaodubsNIdd4/Ls9wMhju3b649u3bqgguHavIX00psn+EeExfkc6ELJeq9mwbo6IG9p9yuTf/hGnTv83mWpyQe09vX9VF6SpI6DLtG/W/+V5GeN27/Hk26eWCRtr3gnufUdsAlRdoWZ+76DnU5jB4kMTFBd1x7mZb9+Yf1dUhoqMLCwnU07nD2Nrff97DuGJfzs7dn1w4N6pFTudLcp1y58kpIOKbUlBSXCkyP/+c1K7QLz9Cunh24L8Zpv9cwHzgw5/rbt2+3vjbjhYyMDKWc+J7s0KGD5s2bp0qVKp3W43/11Ve65ZZbdPToUevrsLAwhYSEZH9tHD58WBUrVnTr2MHn3quoX6RZ/5YOrMu7LiBY6nKL1OcBKayi9NsE6dcXzFFw3a7jtdLgiVJwWKntNgAAgD/y1XNSXxs7+PJ7BQAAAO9QlPPRIldIGj9+fHYY6eWXXy5yGMm47rrrrE8smx257LLLrBP7rGDH119/rZtvvtn6evDgwXnCSFlBKNPKLTExUUOGDNGyZcus5eYTz2+99ZYVWDLMCX/uMJLRu3dvnXXWWdb866+/rnHjxmnvXvsieXJyslV1qU+fPlYYyRwwsx6+Kfenasz8wQP7C53MBdb8/DZ/nhVGMq6+dkyp7T/gT/75bYYVRjLa0a4NcLvw8HL6YMoMPTHhNatiUtmy4UpMPGa1aBt80eX65Ns5LmEko0rV6nr46Rd14fARatK8pcqXj1D80TgFBQWrUdPmumTkNfp42mw9/dJbhJFQKkxlr2HDhlkXFGrVqqU5c+ZYlVPNmOHLL7+0Kp+uWLFCo0fbbQqLy4xTrrrqKusCghlnmA9GJCUlWeMGs+y3337Tfffdp+DgYLe/Np/TpL9020Lpotek8jVc12WmSUvekF7tYFdN+tUElXMNYIPCpIvfsO9LGAkAAACngbEDAAAA4MEVknbu3Gm1JciqLFOtWuGVZUyAyEy5mQsF/fr1c/kEggmFmFBQUT6BYEJIF1xwgWJiYqyvzQUGc9+sFgmmVdsPP/yg0NDQPPeNioqy1m/dujV7mWnnYC5WZIVUTHUlE7S6++675U5USAKcR4UkwHlUSAJ8q0KS+WDAxo0brU/8mhrNgtYAAML0SURBVEqjhTHbme1btWrl1vBOVhVWY9GiRerevbvL+i+++MIKFBVUhbUw+/btU5s2baxPMJsPZZTWhxb84hPOKcekP/4nLXpNSrfDxwWq1FAa8YlUq31p7R0AAIDfK4lzUqfHD744dvCb8QMAAAB8v0LSyZVl9u/fX+h07FjeyjKmzdrq1av12GOPqW3bttbOmQFFp06drPZvS5YsKbQcqtnOfCrZfAq5WbNmVhCpXLly6tWrl9577z39/PPP+YaRjCZNmljP/corr1hlWatWrWqFmUzZVDMAuvXWW7V8+XK3h5EAAAAAX2TaEZgPFPz3v/895bbPPvuste0333zj1n2YPHmydWs+9HDyBQVj1KhRatSokTX/8ccfF+uxTZtnc0HB7LcZf8CNQstL/R+R7lomnWUu+tiD1jyaD5ZuWUAYCQAAwAc4PX5g7AAAAAB4cIUknHkqLDoulcMIOIQKSYDzqJAE+FaFpOHDh2v69OlauHChevToUei2v/76q/r3769LLrlEU6dOdcvzm0qnpmKq+bDEhAkT9OCDD+a73R133GG1eDbtn80nl4uqTp06VptnPuFcCvaulGb/W9q+0P66TIDU/1Gp572mPG9p7AEAAABKuOqOk+MHXx07GFRIAgAAgE9USAIAAACALGvXrlVQUJC6dOlyyoPSs2dPa9s1a9a47QBu2LAhu4qrqb5akKx10dHRio2NLdJjb9u2zbqgkFWl1ey3ad9Qq1YtqyJr3bp1NXLkSP3xxx9ueS1+r/bZ0nXTpWu+lfr9W7rlV6n3OMJIAAAAPsTJ8QNjBwAAAMA5BJIAAAAAFIsJ7ERGRloXCk7FtGk22xbnU8ZFef7cn0guSO51ue9TmE2bNmXPm9DROeecoy+++EJxcXFWy+c9e/ZoypQp6t27t55++unTfg3IxXySpkl/6dwHpVpncWgAAAB8jJPjB8YOAAAAgHMIJAEAAAAolpCQEMXHxxdpW1Ou9dixY9nlW90h93OHh4cXuF3udUXd38OHD2fPP/roo6pdu7bmzJljvQYTSlq3bp369u1rva7HHntM06ZNK/J+m2NQ2AQAAAD4IifHD946djAYPwAAAMDbEUgCAAAAUCyNGjVSamqqFi9efMptFy1apJSUFDVo0MArjnJWKzjDXDiYOnWqBg4cqIAAe+jUunVrTZ8+XTVr1rS+fvLJJx3bVwAAAMAb+Or4gbEDAAAAUDgCSQAAAACK5bzzzrPCOv/3f/+n9PT0Arcz6x5++GHrk73nn3++245yRERE9nxiYmKB2+Vel/s+RX3sAQMGqGPHjnm2KV++vMaOHWvNr169Wvv37y/SY5tjVtgEAAAA+CInxw/eOnYwGD8AAADA2xFIAgAAAFAsd999t8LCwvT7779b1YNWrFiRZ5vly5db/5Q324SGhuqee+5x21E2rRCy7Nmzp8Dtcq/LfZ/C1KlTJ3u+VatWBW5nKiVl2bFjR5EeGwAAAPBHTo4fGDsAAAAAziGQBAAAAKBY6tatq3feeceaX7hwoc455xwryNOjRw9rMvOdO3e21plPN7/77ruqX7++246yCQpltVBbu3ZtgdtlrTPt1SpXrlykxzZBo8DAwFNul7uikXmNAAAAADxv/MDYAQAAAHAOgSQAAAAAxXbNNdfohx9+UIMGDaxwzr59+7RkyRJrMvNmWePGjfXTTz9p9OjRbj3C4eHh6tmzpzU/c+bMfLcxzz9r1ixrvjjtHswnt/v06WPNb9iwocDt1q9fb92aCyYNGzYs1v4DAAAA/sap8QNjBwAAAMA5ZY7n/mgv3H+AT3xaOjoulaMLOGTigiiOPeCw6zvUdXoXAL/Vrl6EdVtSp/0ZGRmaP3++Fi1apOjoaOv811QkMp907tevX3YlI3d7//33ddNNN1nPt3jxYnXt2tVl/ZQpUzRy5Ehrfu7cuVb7h6L65JNPdO2111qPvXTpUnXs2NFl/bFjx9SsWTPr9Xbr1s16fneOHRiiAQAAwCklfU7qxPjBF8cOBuMHAAAAOKko56MEkkrpTSCQBDiHQBLgPAJJgO8GkpySnp5u/bN/zZo1VouHyZMnWxcOMjMzNXXqVOuCw9GjRzV48GDNmDHD5b5PPPGEnnzySWt+27ZteSocmcfo3r27/vrrL2vdpEmTsi+OmKpJY8eOtS6imK/nzJmj/v37u+U1cUEBAAAATvPFc1JfHDv46nsFAAAA71GU89GgUtwfAAAAAHCLoKAgq+WD+Wf/9u3bNXDgQKsdg7kgkJycbG3ToUMHffbZZ8V+bHOx4Pvvv7cuUpjWbFmPHRwcrLi4OGsbM//GG2+49YICAAAAAPdj7AAAAAA4o2T6JwAAAABACTOfQF69erUee+wxtW3b1vpEhgkKderUSS+++KKWLFmiSpUqndZjm7YRy5cvtx6nc+fO1uMmJSVZzzlmzBhr3c033+z21wQAAADA/Rg7AAAAAKWPlm0lfYBp2QY4jpZtgPNo2Qb4Zsu2lJQUq8XB77//rt27dyshIaHA5zHnxfPmzXP7PvgSWi4AAADAl89JGT+4F+MHAAAAOImWbQAAAABKxKJFizRy5Ejt3bvXulhx8uAj6+usZbm/BgAAAOBfGD8AAAAA/ifI6R0AAAAA4F127dqlIUOGKC4uTu3bt9egQYM0YcIElS9fXvfee6+io6P1yy+/aOvWrapatapuu+02BQYGOr3bAAAAABzA+AEAAADwTwSSAAAAABTLyy+/bIWRBg8erB9//NGqfpQVSHrqqaeyt3vrrbd09913a9WqVfr+++85ygAAAIAfYvwAAAAA+KcAp3cAAAAAgHeZPXu2FUJ68sknC23Fdvvtt1vbmNDSe++9V6r7CAAAAMAzMH4AAAAA/BOBJAAAAADFsnPnTgUEBKhjx44uy1NTU/NsO3bsWCu09NFHH3GUAQAAAD/E+AEAAADwTwSSAAAAABTL8ePHValSJSuUlKVcuXI6evSotS63yMhIa9q4cSNHGQAAAPBDjB8AAAAA/0QgCQAAAECx1KlTR/Hx8S7L6tatq4yMDG3YsMFleWJioo4cOWLdAgAAAPA/jB8AAAAA/0QgCQAAAECxNG7c2GrPFhUVlb2sa9eu1u3bb7/tsu3LL79sfSK6YcOGHGUAAADADzF+AAAAAPwTgSQAAAAAxdK3b18rZDRnzpzsZTfddJO17I033tCFF16oRx55REOHDtXjjz+uMmXK6Morr+QoAwAAAH6I8QMAAADgn8ocN1cNUHIHuEwZ6zY6LpWjDDhk4oKc6g0AnHF9h7ocesAh7epFWLfuPO3fvn27brjhBnXo0MGqgJTloYce0sSJE7PPg7Oes0+fPpo9e7ZCQkLctg++PHZgiAYAAABfOidl/FAyGD8AAADA089HCSSV0ptAIAlwDoEkwHkEkgDfCiQVZu7cufryyy+1a9cuRUZGatCgQbr22msVFBRUKs/vzbigAAAAAH87J2X8cPoYPwAAAMBJBJI8AIEkwHkEkgDnEUgC/CeQhNPHBQUAAAA4jXNS78F7BQAAAE8/Hw0oxf0BAAAAAAAAAAAAAAAA4OMIJAEAAAAAAAAAAAAAAABwmyD3PRQAAAAAfxIfH6/p06dr9erVOnz4sNLS0got3/r++++X6v4BAAAA8ByMHwAAAAD/UuZ4YQ3d4La+edFxqRxNwCETF0Rx7AGHXd+hrtO7APitdvUirFt3n/a/++67euCBB5SQkJC9LL/nMOfDZrm5zcjIcOs++GPPbQAAAMAbz0kZP7gf4wcAAAB4+vkoFZIAAAAAFMuUKVN02223WfPlypVT9+7dVaNGDQUFMbwAAAAAwPgBAAAAAIEkAAAAAMX04osvWreDBw/Wl19+qYgIuwoTAAAAADB+AAAAAGAEcBgAAAAAFMe6deuscqwffPABYSQAAAAAjB8AAAAA5EFPBQAAAADFYtq0hYaGWm3aAAAAAIDxAwAAAICTUSEJAAAAQLGcddZZio+PtyYAAAAAYPwAAAAA4GQEkgAAAAAUy7333quMjAy98cYbHDkAAAAAjB8AAAAA5EHLNgAAAADFMmTIED366KPWZNxzzz0qW7YsRxEAAAAA4wcAAAAAljLHjx8/bs+iJJQpU8a6jY5L5QADDpm4IIpjDzjs+g51nd4FwG+1qxdh3ZbEaf/EiRP10EMPWWGk1q1bKyLCfq6CzovnzZvn9n3wxbEDQzQAAAD44jkp4wf3YvwAAAAATz8fpUISAAAAgGJ75JFHNGHCBGvQkZSUpGXLlhVpcAIAAADA/zB+AAAAAPwPgSQAAAAAxfLBBx/o+eeft+YbNWqkAQMGqEaNGgoKYngBAAAAgPEDAAAAAAJJAAAAAIrp9ddftyoeXXfddZo0aZICAgI4hgAAAAAYP/gD05KDCrgAAMAJGWlSwiHp2H7p2AH7NuGAVK6a1H6UFBTC++JhyhwviWbQyDnAJ07Mo+NSOSqAQyYuiOLYAw67vkNdp3cB8Fvt6kVYt+487S9fvrzVpi0mJkYVK1Z02+P6u6L03AYAAAC87ZyU8YOPjR/mPinFbJG63ynV60I4CQDgv8zf4AMbpLAKUiTXQE5bZqaUdDgnXJQVNModOsq6TYw1Bz7/x2nYW7rqKymk3OnvC9x+PkpPBQAAAADFUq5cOYWEhBBGAgAAAMD4wZ+kHJP+fl9KiZM2/CDV6SR1u0NqfbEUGOz03gEAUDoyM6T130u/vyJFr7aXNR0odR8rNe5HWNcwAZXUYyeFiw7mHzQyIaTM9DN/X7YvlD4fKV01RQoJP/PHg1sQSAIAAABQLF26dNHPP/9sVUiqUqUKRw8AAAAA4wd/sPJzO4yUZc8yaeqN0pzHpa63SB2vk8pSRRcA4KPSU6RVX0h//E+K3eq6bstce6re2g7rtrtCCg6Tz9u70n7dR/eeVN3ogJSWWPr7Y4WSRhBK8iC0bCvpA0zLNsBxtGwDnEfLNsC3WrYtXLhQ/fr10913362XX37ZbY/r72jZBgAAAF88J2X84EPjh0WvSQsmSClH818fXE7qMFrqdptUuXHp7RcAACUpJV5a9pG0+A0pfl/R7lOumtT5ZqnzjVK5qr71/pj2amu+kZZPlqLXlM5zhkRI5atL5WvkvTX78Nc7+bRvo1KSJ5yPEkgqpTchOi61pJ8KQAEIJAHOI5AE+FYgyXj//fd155136rrrrtNDDz2kRo0aufXx/RGBJAAAAPjqOSnjBx8aP5iLsis+lZa8JR3ZUcBGZaSWQ+wKEQ160LoG8EUmjLDyM6lSI6njNVLtDk7vEUpLaoK0Y7G07Vdp+x/S8Qy7TdnZV0nVWvjW+5AQYwdd/nxHSj6S/zaVGtotTRMP5b8+MFQ6a6T9N7F6K3ktc76x/XdpxSd2u7r05DN/THNsXAJG1U76+sR8ueqFt2Az+zbvSbuFXm6N+khXfkX7thJEIMkDEEgCnEcgCXAegSTAtwJJjRvbn3Q9cOCAkpKSrPnKlSsrIsJ+roLOi6Oioty2D76IQBIAAAB88ZyU8YOPjh8yM6SNP0lL3pR2Li54u1pnSd3vlFoPl4JCSnMPAZQE8ztn/rPSbxNdl9c6WzpnjNT2Mim0PMfel2SkS3tXSFt/taddf0qZaflva4JpZ10ptb1cKldFXitut7TodbsCUEFtx2q0k3rfZ/99y0iVVk+x/yYe3Fjw4zYZIHUfKzXp7z1h3fhou2WrCSKd3KYuP2UC7OpQJ4eKcoeLsubDIt13HAoMJZ0rXfkloaQSQiDJAxBIApxHIAlwHoEkwLcCSQEBAad1XpyRkeG2ffBFjl9QAAAAgN8riXNSxg9+MH7Ys0xa/Ka07lu7UkZ+ImpJXW6ROl0vhVcu7T0E4K4g4k/j7NZVhbVVaj9COucGqWY7jrs3Mn9XDm3OCSBtX1hwq86CBARJzS6Qzr5Sana+FBQqr2Be9+//lVZ/VXDoqn4Pqfc4qenAvGEac+yi5tmt3aJ+Kfh5qrWSut8htRshBYfJI0NoW+ZIyz+RNs0s+G+7Ua2l1PFauxpR+Zr23/iAQDnCHP+5T0h//Nd1OaGkEkMgyQMQSAKcRyAJcB6BJMC3AkmTJ08+rfuZ9m7wkgsKAAAA8EslcU7K+MGPxg+mosRf79phheS4/LcJDrfb+nS9XaratLT3EMDpSkuWpt4obfyx6Pep21nqdIPU5hKqk3i6o/ukbQtOhJAWSPF7i3Fn8/eokL9FZSvZlbPOukqq09EzKwOZClALX5Y2TC/4tTQfJPW6T6rfrWiPuX+9XTHJVE7KSMl/m/CqUpebpXNutNuVOc1UQDJtWVd8Jh2LLni74HJS20uljtdJdc/xrPfUCiU9Lv3xP9fljfvalZKCyzq1Zz6JQJIHIJAEOI9AEuA8AkmAbwWS4EcXFAAAAOBXOCf1Hh79XqUck1Z9YV+ILbC9Sxn74q6pENGwt2ddzATgygQMv7hK2vG763LTdspMJoQYs6Xgo2baMpk2XiacVL0lR9cTJB+VdvyRUwWpsDZjJ6tYX2rczw54mKo46Sl2RSHze//QpsLvW6WZdNYoqf1IqWI9Ocr8/dz2m/T7y/YxKKj9mAlT9bxXqtn29J7n2EFp6fvSX+9JiYfy3yYw1K4sZtq5VW+lUg8bmiCWaU9nqmGdKmRoqiGZkGGo/T9nj0QoqdQQSPIABJIA5xFIApxHIAlwDoEk7+HRFxQAAADgFzgn9R5e8V6Z9k6bZtmta04OMuRm2jp1G2tf9A0KKc09BHAq8dHSp5dL+9e4Lm97uTT8Lftn1vweMkGGpR/awYaCWl0Z9btL54yRWl3kma2qfFV6qrRnaU4AaffSwttwnVzhyLS8MgEkM1VulP925vvAVBoywaQ130hJsYU8aBmpUW87qGa+F0LLq9RkZkr/zLCDSKblaEEBoQ5XSz3uLvj1nk7wZ80U+29iYQEwE/IzwaQmA0o2rBu9xm7JZsJkyUcK3q5sZTtE1uEaqUZreQ3z/TjnMWnRq67LqZTkVgSSPACBJMB5BJIA5xFIApxDIMl7eMUFBQAAAPg0zkm9h9e9V3tXSkvektZ+I2Wm579N+Rp265pOY6RyVUp7DwGcLCZK+uQS6cgO1+Wm5eIFz0kBAflXg1n5qV016fD2wkMOpn1jp+ulqs049u5m/jbsX2eHj0wrtu1/SGkJRbtvUJjUoEdOAKlGu/zf61MFoDbPtsNJJphaWEjNtPI0oSQTejEVlwICVSIy0uyg1B//LTgQFBIhdb5R6naHFFGj5N6bqF/sYFLUvIK3q9ZK6na7XU3KXeE9UxnL/B1e/rEdHiuMqYJlqiG1HCIFhcorWaGkR6VFr+V9bVd+Qfs2NyCQ5AEIJAHOI5AEOI9AEuAcAknew+suKAAAAMDncE7qPbz2vTq6T/r7PWnpB1LS4YIvhpsL0+aCcLUWpb2HALJChJ9dLiUcdD0eAx6Teo07deUWU4Vm26921aSNPxVejce0bTznBqnlMKqknYkju3IqIJkQ0snvXUFMW7LaHXICSHW7uLd6VWKstHaqHU4qqCJRlgp17NZlpnKSu37/pyZKKz6xQylxu/LfJryqHf7pfJNUtqJKzYENdnvTVV9JGSkF75vZLxOUKl+9+M9hzhN2LrFDSOu/k9ISCz/+HUZLZ18tVWogn0AoqUQRSPIABJIA5xFIApxHIAlwDoEk7+G1FxQAAADgMzgn9R5e/16ZC8Tm4rSpmhSzueDtmp4ndb/DrmZQkq1rAOTYukD68mopNd41tDL0v1Kn606v7ZsJhCz7WIrbWfB25arZQQhTNcldbbJ8Wcoxu9JOVggpNqro963SNCeA1LCX3ZatNBzcZP/uN23Cju4pfNvaHe1gkmnneTpV85KO2AHYJW9LiYfy3yaynt2WzYRwQsLlGFNZbOn70l/vFbyvpo1c+yvsFqdFaZ1mHtMcaxNEKuzvbECQ1GKw1PE6u11cSVWocpI5V5r9b2nx667Lzesd9TmVks4AgSQPQCAJcB6BJMB5BJIA5xBI8h5ef0EBAAAAXo9zUu/hM++VqaKyZY7dusZU9ChI9TZ29Yp2V7i3cgcAV+u+k6bdLGWkugYhLv9AajX0zI5WZoa0ZZ607ENp00zpeGbB25oQ4jlj7KBEYDDvUpbkOLsF2vrvpS1zpfTkoh2bctVzAkiNz5Ui6zr/u3/7Qjsws/6HwtvJBQRLzS+wK+c1u+DUVbRMAM5UHfr7A9dQXW7VWkq97rPDTp70/ZWWLK352v6beHBDwduZII0JJjUd4BrWNT9jJqS2fLL0z88Ft0g1qjSzW7KZ43o6lZd8JpQ04EQoiXOL00EgyQMQSAKcRyAJcB6BJMA5BJK8h89cUAAAAIDX4pzUe/jkexW9xq6YZC7G5g5DnFxBxbSu6XCNPX+qC9MAiu7v96Wf7je/WXKWhUZKV34hNezp3iMZt8eummSqtxRWKad8Dfvn3VRmqlhffsm0tzThEhNCMmGTgn4/5hZczq58lBVCqt7Kc6vMmUpPG3+UVn4ubfvN9fvvZKaSU9vL7cpJdTq6vqbYbdIf/7Mfp6D2Z3XOkXqPk5oPlgIC5LHM33bzXptglQmeFcQEq0xYt0FP+2/nik8L/3kKKiu1vdT+marfzXO/J0oKoSS3I5DkQW/C3iMF/OIDUOJCgzz4pALwE0cS05zeBcBvNake7nv/pPZRPnlBAQAAAF6Fc1Lv4dPvVfx+6e9JdvuaxJjCtzWtZkLK2RffTbudYs2f+Dq/+eBwz75YDbiT+T2y4D/Sr8/nDQONnibVbFtyxzsjXdo8266atHlOIWGUMlKz86RON0jNzpcCg+TTEmKkf36yQ0imHVthlW6MMoFS3c45AaQ6nbwzsBm3W1o9xa6cdGhT4dtWbW5X96nbRVr2kbRuWsFVt0zFLRNEatjb+0I4BzbYwaRVXxUctDqV2h3sakimIlRYpOTvv+9mPSItecN1OZWSTguBJA9AIAlwHoEkwHkEkgDnEEjyHj59QQEAAABegXNS7+EX71Vakn1h2lyIPbix9J/fVJIoKLQUWt5ugRRR055McCNrPrSC913whv8yLZ5+Hm+HAHOr3ES6ZppUqWHp7cvhHXbFJFM56dj+grerUMcOV5gqL5F15DOOHbArBZkQ0raF0vGMwrc31YJaDpVaDrGrIYVGyGeYv217l0urvpTWfCMlxZ7Gg5SRWl9kt2YzgRxvd+ygtPQD6e/3pISDp97eBI/aj5I6XiPVbFcae+hloaR/2ecXuTUdKI38jPZtxUAgyQMQSAKcRyAJcB6BJMA5BJK8h19cUAAAAIBH45zUe/jVe2W1rpknLX7DbmHj6UyQKaKGVL5m/oGlrOUmTEBwCU5KT5Gm3SKt/851ea2zpau/kcpXc2a/MtLsFmWmalJhP/NlAqQm/e3KQCZwYabIet71c3V0n7Rhuh1C2rmo4Ao/WcKrSq2GSa0vtkNIgcHyeempdhUtUzVp0ywp8xTdGAKCpbNGSj3vlao2k89JS7bbs5kwzYH1edc36iN1uFZqNVQKLuvEHnoHQkluQSDJAxBIApxHIAlwHoEkwDkEkryHX11QAAAAgEfinNR7+O17ZVrXrPhUit0mpSVIqWZKlFKPSWnmNtFe7g0CQ3KCStm3+YSYTACB9nFwt5R46curpW0LXJc3Olca9ZnnVNuJ3Sotm2z/3CceKlpVmBonwkmm1Zy5rdZSCgqVR7UlW/+DHULa9WchbepOML8XTKUfE0Kq310KCJTfMq3sTGu2lZ/bFZRyM602O10vdb/TtypnFcT8/d86X/prknRkp9T8fKnDaKlyY6f3zLuO4cyHpT/fcl3e9Dxp5KdUSioCAkkegEAS4DwCSYDzCCQBvhtIyszM1ObNmxUbG6u0tMI/odSnT58S2Qdf4bcXFAAAAOA356SMH9yH8UOh32hSelLeoFK+8ydCTUWdz0hVqSsTeCKclFV1yczXkup3s8Mj3lQNBp7T+umzy6R9q1yXt7lEuuQdzwrv5K6Ss3G6tPRDafvC4t03IEiq2iKnipIJKpnQUrkqKjWHt+eEkPYsPfX2pi2dCSCZqW4XQon5OfiP3dJt30qpXjepy81SeGX3v3fwbYSSzgiBJA9AIAlwHoEkwHkEkgDfCyTt27dPDz/8sL755hslJSUV6bw4PT3drfvga7igAAAAAF89J2X84H6MH+RcKykTTkqOk44dkI5FS/EnJmt+f86yBFPNpYQ/cGLaVV34olSlSck+D3yHCcZ8coldeSi3zjdLg//jHdV3Dm2x27mZFl6JMaf/OCb0U+NEFaWsqVIj94V/YqLsdngmiGRCM6dSsf6JENJwqXZHQkhAqYaS/k/6823X5c3OtysleWJI00MQSPIABJIA5xFIApxHIAnwrUDS3r171bVrV+u2OI9rPg2NgnFBAQAAAL54Tsr4oWQwfvCS8FLCwRNhpf1S/L5cgaVcwSUTbDqecfrPExgq9b5f6nUvF01RuOi10qeX2t+PufX9l3TueO+rtmX+z3RkhxS9xp72r7Vv43ad/mMGlztRQSkrqNReqt5KCrH/v1akqj2mCpKZzP6cimmvZQJIpiVbrbO97z0AfAWhpNNCIMkDEEgCnEcgCXAegSTAtwJJt9xyiyZNmqSIiAg9++yzuvjii1W7dm0FBnrBp+g8GBcUAAAA4IvnpIwfSgbjBx+SmWFXeck3uJS17MRtYS3jqjSVhrwkNe5bmnsPb7H9D+mLK6WUuFwLy9jfM51vlE9JjJX2r8sVVFojHdgoZaad3uOVCbB/vkxAyQoqtbfnTftE8/fywPqcENLBjad+vKrNT4SQLpZqtCGEBHgK8/P880PSX++4LqdSUoEIJHkAAkmA8wgkAc4jkAT4ViCpXr161qecv/rqK11++eVue1x/xwUFAAAA+OI5KeOHksH4wQ+Zn8ukw3Y4acsc6df/SGkJebdrd4V0/rN2WAIwNv4kfX2DlJGSczwCQ6TLJtmhGH+Qniod+seuEmUFlVbbFYzMz9TpKldNCilnt8E7leptTrRju1iq3vL0nxOAQ6GkC6SRn1CJ8CQEkjwAgSTAeQSSAOcRSAJ8K5AUFhZmPV5CQoKCgoLc9rj+jgsKAAAA8MVzUsYPJYPxAxS3275ouvHHvAcjNFIa+JjU6QYpgGrGfm35x9L0e6TjmTnLQiKkKz+XGvWRXzN/647uORFQMkGl1fb84W3uefxaZ9kBpFYXS1WbuucxAZRSKGm89Ne7rsubD5JGfEwoKRcCSR6AQBLgPAJJgPMIJAG+FUiqX7++jh49qiNHjrjtMcEFBQAAAPhmyIXxQ8kgkIRs//wszRgvxe3Me1DqdJKGvmIHI+BfzO/x31+W5j2Vt6rP6Kl8TxQmJd615ZuZTFu29ORTH3fzM2eFkC6SKjc6wzcRgKO/Q2c8KP39nutyQkkuCCR5AAJJgPMIJAHOI5AE+FYgacyYMZo8ebI2btyoZs2aue1x/R0XFAAAAOCL56SMH0oG4we4SE2QfpsoLXpNykw/6ZslQOpyq9TvX1JYBQ6cP8jMlGY9LP35tuvySg2l0dOkKk2c2jPvlZEuxUbltHvLav1mWr7V6XgihDRMqljf6T0FUOKhpMHSiMlUShKBJI9AIAlwHoEkwHkEkgDfCiRt2bJFHTt21MCBAzVt2jS3Pa6/44ICAAAAfPGclPFDyWD8gHwd2CD9OE7auSjvuoha0qAX7ODEiZ91+KD0VOm726W137gur9HOrowUUcOpPfNN5u8lP0+Aj4eSHpD+nuS6nFBSkc9HAwpcAwAAAAD5aNq0qX744QctWLBA5513nubPn6+EhASOFQAAAADGD4CTqreSbpghXfymVLay67r4fdLX10mfXS7FbnNqD1GSUo5JX4zMG0Zq0Eu64SfCSCWBMBLg+z/jF74onXOj6/JNP0tTrrNDoE5VbTu6V9q9TNrwo/TXe3aLzqj58jRljrvzow7Ie4BP/CHaeySFowM4hApJgPOokAT4VoWkwMDA0zovTk8/qWw88hwjgyEaAAAAfOmclPFDyWD8gFNKjJXmPCat+CTvuqAwqc8DUo+7aTnjKxJipM+vkPYsc13ecqh02ftScJhTewYA3s+cG/90v7T0fdflLS6UrjDt20Lc9zzJcXaI2ASO4qOl+L3S0X2u8wkHpOOZee/f817pvCflSeejBJJK6U0gkAQ4h0AS4DwCSYBvBZICAgJO67w4IyPDbfvgi7igAAAAAF88J2X8UDIYP6DIdiyWfrxPOrgh77qqzaUhL0uNenNAvdmRndInl0oxm12Xd7xOGvqKFFD8D5YBAE6SmSnNMKGkD04vlJSeciJUVEjYyNymJZ7+oW8/Urr0XY86Hw0qtb0BAAAA4BNMizYAAAAAYPwAeIEG3aXbFkpL3pR+fcH1QuehTdLkoVL7UdL5z0jlqzm5pzgdBzbYYSRzITu3Pg9K/R6hpRgAuIv5kO6FL9nzuUNJ/8yQvr5e6vuQHSgqKGyUGFPy74UJO3kYKiSV9AGmQhLgOCokAc6jQhLgWxWSUDL4hDMAAACcxjmp9+C9wmlX0vn5Ifvi6cnCIqWBT9pVdU6jMjIcsPNP6fMRUvKRXAvLSIP/I3W9lbcEAEqqUtJP46RlH5b+8Q0IliJqSRVq2bfZ87WliJpSxfpSpQaltju0bPMABJIA5xFIApxHIAlwDoEk78EFBQAAADiNc1LvwXuFM7LxJ2nGeOno7rzr6na223zVbMdB9mSbZklTrpPSk1wvVF/6jtT2Mif3DAD8JJR0n7TsI/c9ZngVO1hkBYxq5prPCh7VlspW9qjQMIEkD0AgCXAegSTAeQSSAN8PJGVkZCg2Ntaar1y5sgIDA0v0+XwRFxQAAADgL+ekjB/OHOMHnLGUY9KC/0iL35COZ5z0DRYodbtd6vt/UmgEB9vTrPxC+n6s6/sWXE4a9anUpL+TewYA/qOooaSgsq5VjFzmze2JAFJQqLwNgSQPQCAJcB6BJMB5BJIA3wwkJSYm6u2339YXX3yh1atXKz093VoeFBSks846S1dddZVuueUWhYfb+4DCcUEBAAAAvnxOyvjBvRg/wG2i19qtZ3b9mXddhTrSoBekVsPMNx0H3RMsek2a/e+8VTWu/kaq09GpvQIA/w0lLX1f2rFIKlspb0WjiFp2S1Qf/RtKIMkDEEgCnEcgCXAegSTA9wJJ//zzj4YNG6aoqKgCH9ucCzdt2lTTp09X8+bN3fr8vogLCgAAAPDVc1LGD+7H+AFuv6C68lNpzmNS0uG865tdIF04QarUkAPvFPN7ee7j0h//c10eWV+65lupalOn9gwA4KfKFGHsUOZ4Sdde9XMEkgDnEUgCnEcgCfCtQFJ8fLzatm2rXbt2WdWQLr30Up133nmqW7eutX737t2aO3eupk6dalVNatCggdasWaPy5cu7bR98ERcUAAAA4IvnpIwfSgbjB5SIhEN2KGnlZ/m3nDn3Qan7XVJQCG9AacpIl6bfY4fGcqveWho9za7IAQBAKSOQ5AEIJAHOI5AEOI9AEuBbgaSnn35ajz/+uGrXrq0ff/xRZ599dr7brVq1SkOGDNG+ffv01FNP6ZFHHnHbPvgiLigAAADAF89JGT+UDMYPKFHbf5d+HCcd+ifvuqotpKGvSA178iaUhrQk6Zsx0j8zXJfX6ypd9ZXdIggAAAcQSPIABJIA5xFIApxHIAnwrUBSp06dtHLlSv3www9W4KgwM2bM0NChQ9WhQwctW7bMbfvgi7igAAAAAF88J2X8UDIYP6DEpadKi1+XFkyQ0pPyrm97uVSjtRQUJgWFSoGhOfOnvD0xHxDIG1mYpCPSF1dKOxflbaF3xUdSiP0/HwAAnEAgyQMQSAKcRyAJcB6BJMC3AkmRkZFKS0tTYmLiKbc1z1uuXDkFBwcrLi7Obfvgi7igAAAAAF88J2X8UDIYP6DUHN4uzXhQ2jzb/Y8dEFR4eCkwpOD1oRWkFoOlWu3lk+KjpU8vk/avdV3efpR08etSYLBTewYAgIVAkgcgkAQ4j0AS4DwCSYBvBZLCw8MVEhKiI0eOFGn7ihUrKjU1tUgBJn/GBQUAAAD44jkp44eSwfgBpcr8TtgwXfr5ISl+rwcd/DJSj7uk/v+2Q0q+Inar9Mkldhgst+53Suc9LQUEOLVnAAAU63yUv1gAAAAAiqVu3bqKj4/X+vXrT7nt2rVrdfToUes+AAAAAPwP4wfAB5gLjq0vku78yw7FlPGUVmvHpUWvSpMGSAc2yifsWyW9f0HeMNLAJ6XznyGMBADwKkFO7wAAAAAA7zJgwABt2bJFt99+u2bNmqWwsLB8t0tOTtYdd9xhfVJi4MCBpb6fAAAAAJzH+AHwIaER0gXPSr3GSQfWS+kpUnqylJGSM1/gbeqJ28K2Scl5rLQkO3BUFNFrpHfPlc57Supyix2g8kbbFkpfXiWlHM1ZViZAGvaq1PEaJ/cMAIDTUua4O2uvIu8BPnHSs/dICkcHcAgt2wDn0bIN8K2WbVu3blWbNm2sNmxNmzbV+PHjrYsMderUsdbv3r1b8+bN08SJE7V582aFhoZq3bp1aty4sdv2wRfRcgEAAAC+eE7K+KFkMH6AzzO/hzLT8w8upSVLf74lrfk67/2aDJCGvylF1JRXMS3xvrnRDmRlCQyVrvhQajnEyT0DAOC0z0cJJJUwAkmA8wgkAc4jkAT4ViDJmDJliq655hqlpaVln/OezDxncHCwPvnkE40YMcKtz++LuKAAAAAAXz0nZfzgfowfAEmrv5Z+ul9KiXM9HGUrSxe9JrUa6h2Hadlk6cd7peOZOctCK0hXfiE17OXkngEAcEbnowEF3x0AAAAA8mcCRosXL9YFF1yQPejIPZnByODBg7VkyRLCSAAAAICfY/wAoES0v0K6/Q+pwUmhnaRY6aurpR/uklKOee7BNxdwF74sTb/bNYxUrrp0/U+EkQAAXo8KSSV9gGnZBjiOCkmA86iQBPhehaTc4uLitHz5ch04cMD6unr16urYsaMiIyNL7Dl9EZ9wBgAAgD+ckzJ+cA/GD0AumRnSotekX56RMtNcD03lxtKl70l1z/GsQ5aZKc3+t7TkDdfllRpK13xr7zcAAB6Mlm0egEAS4DwCSYDzCCQBvh1IgntwQQEAAABO45zUe/BeAfnYu1KadrN0aNNJPzCB0rkPSb3vlwKDnD90GWnS92Ol1V+5Lq/RTho9VYqo4dSeAQBQZLRsAwAAAAAAAAAAAOD7ap8t3bJA6nyz6/LjGdKvz0kfDpZit8pRqQnSF1fmDSM16Cnd8BNhJACATwlwegcAAAAAAAAAAAAA4IyFhEtDXpSu+loqV9113e6/pLd7Sys+NaWkS/9gJ8ZKHw+XtsxxXd5iiDR6mhQWWfr7BABACSpznN4NJYqWbYDzaNkGOI+WbYD3tmxr3Lixddu0aVPNnj3bZVlxz4ujoqJOax/8BS0XAAAA4O3npIwfSg/jB6AIEg5JP9wl/TMj77pWF0nD/ieFVy6dQ3l0r/TJpdLBDa7LO4yWhv7PM1rJAQDg5vNRAkkljEAS4DwCSYDzCCQB3htICgiwi6q2bNlS69evd1lW3PPijIyM09oHf8EFBQAAAHj7OSnjh9LD+AEoIvP7bPlkaebDUlqi67qIWtLwN6Um/Uv2cB7aLH1yiRS3y3V5r/ukAY+bH+iSfX4AABw6HyVuCwAAAKBAH374oXUbGRmZZxkAAAAAMH4A4NHMxdJO10sNeknTbpb2Ls9ZF7/PDgp1u8MOBgWHuf/59yyXPrtcSoxxXX7+s1KPO93/fAAAeBAqJJVSKmzvkZSSfioABaBCEuA8KiQB3lshCaWHTzgDAADAaZyTeg/eK+A0ZKRJCyZIC1+Ujme6rqveWrr0PalmW/cd2qj50lejpdRjOcvKBNpVmc4a5b7nAQDAQ89Hi99rAQAAAAAAAAAAAAC8SWCw1P8R6YafpYoNXNcdWC+9109a9LqUeVJY6XSsnSZ9doVrGCmorHTlF4SRAAB+g0ASAAAAgGIZM2aMxo0bV+Ttx48frxtvvJGjDAAAAPghxg8APE79btJtv0tnXeW6PCNVmv2I9Mlw6eje03/8vydJ34yRMtNyloVFStd+LzW/4PQfFwAAL0PLtpI+wLRsAxxHyzbAebRsA3yrZVtAQIBq1qypvXuL9s+5Ro0aaefOncrIyHDbPvgiWi4AAADAF89JGT+UDMYPgJus+1aafq+UfMR1eVhFadj/pDbDi/5Y5nfngv9Ivz7vujyiljR6mlSjtXv2GQAAD0DLNgAAAACOc+fFDAAAAAC+jfEDgFLV5hLp9kVSo3Ndl5uA0tfXSd/dISUfPfXjmDZvMx7MG0aq3EQaM4swEgDAL9GyDQAAAECJOnTokMLD7UpN7hYfH68nnnhC7dq1U/ny5RUZGanOnTvrpZdeUmpqqluf67bbbrM+9WGmhg0buvWxAQAAAJTs+IGxA4ACRdaRrvlOuuA5KTDEdd3Kz6S3e0k7lxR8//RUaeqN0t/vuS6vdbYdRqrUgIMPAPBLQU7vAAAAAADfFBcXp0mTJikxMVHt27d3++Pv2LFDffv21fbt262vzUWLlJQULV261Jo+++wzzZs3T5UqVTrj55o/f77effddN+w1AAAAgNIePzB2AHBKAQFS97F2paRpN0sH1uesO7JD+nCw1Pt+6dyHpMDgnHUpx6SvRktb57s+nnmcUZ9JoREcfACA36JCEgAAAIBCPfnkkwoMDMyejP3797ssy2+qXLmyxo8fb1UUuuyyy9x6lNPT0zVs2DArjFSrVi3NmTNHCQkJ1sWLL7/8UhEREVqxYoVGjx59xs9lHvPmm29WUFCQzjnnHLfsPwAAAOCrPG38wNgBQLHUbCvdPF/qdofr8uOZ0m8TpQ8ukGKi7GUJMdLkYXnDSK0vlq7+mjASAMDvEUgCAAAAcErHjx/PnswFgtxfFzYFBwdrzJgx+r//+z+3HuXJkydrzZo11vzUqVM1cOBAaz4gIEAjR47UO++8Y309Y8YMq0rSmXjkkUcUFRVlXRxp06aNG/YeAAAA8G2eNH5g7ACg2ILDpEHPS9d8K0XUcl23Z5ndwm3Ra3Y4ae9y1/XnjJEu/1AKCuXAAwD8Xpnj5iwfJcYMtoy9R1I4yoBDQoPIXgJOO5KY5vQuAH6rSfVw6/ZMTvtNe4Ostmjmcfr37299etkEgQpigkEVKlRQ8+bNVbZsWblbnz59tHDhQvXr10+//PJLnvVmP5s0aaJt27bp2muvtS5CnI4lS5aoZ8+eatq0qVatWqXbbrvNeqwGDRpkHxN3jx0YogEAAMAp7jgn9bTxgy+OHQzGD0ApSYyVpt8jbfjh1Nuadm59HzY/oKWxZwAAOKoo56NBpbg/AAAAALyQ+Qe6mbLUr19fNWrU0LnnnuvI/pgWan/88Yc1P3jw4AIHQ4MGDdJbb72l2bNnn9bzpKSkWJ/ONgOqd999V2FhYWe03wAAAIA/8KTxA2MHAGcsvLI04mNp5efSz+Ol1GP5bFRGGjxB6noLBxwAgFwIJAEAAAAolpL4dG9xbNiwQZmZmdZ827ZtC9wua110dLRiY2OtT2UXx1NPPWU910033eRY+AoAAADwdk6OHxg7AHALUwGiw9VSg+7StFul3X/lrAsIli55W2p3OQcbAICT0McIAAAAgFfZu3dv9nydOnUK3C73utz3KYoVK1ZowoQJ1ie5J06cKHcwVZsKmwAAAAC4l7eOHQzGD4AHqtxYuuFnqd+/pfI1pKrNpaunEEYCAKAAVEgCAAAAcEYOHDig3bt3KyEhodB+0X369HHLkY6Pj8+eDw8PL3C73Oty3+dU0tPTrVZt5vbVV19VxYoVz2BvAQAAADg1fmDsAMDtAoOkcx+Uet9vV07iA0YAABSIQBIAAACA0/L6669bgZ2oqKgifbrXBHy8wQsvvKCVK1dq6NChGjFihNset7CLLQZVkgAAAODLfHH8UFJjB4PxA+DhAmhCAwDAqRBIgt9ZvXKF5sz8SatXLtfWqM2KPXRI8fFHFRFRQU2at9CA8wbp2htvUaVKlQt8jIMH9uvN/72kObNmaO/uXQoLK6vmrVrriitH66prbuBiElCIxMRELfr9N61csdz6eVy1crl279pprRv/8KN66JHHTnn8fvh2qr78/BPrMWJjDik4OFi16tRVj569dOMtt6td+7N5D4BievvVFzXxmZyfv6gDiXm2aVK94Eo0J+vas48+/3Ym74MPGzVqlL7++utT/pM8S1G3K4qIiAiXvysFyb0u930Ks379ej399NMqX7683nzzzTPcUwAAAABOjh8YOwAAAADOIb4Lv/PlZx/ppReetkJJUZs3KSkpUWFly+rw4Vgt/XOx/vPM4+p9Tjst/WtJvvc3Qaa+3TronTf+p61bNiswKEjHjsXrr8V/6MG7b9fVlw9Tampqqb8uwFssX/q3Rl46TM8//bh+mv5ddhipKFJSUnT1iEt0wzWjNOvnn7Q/ep9CQkOtT8yZn+dPPvpA/Xt11Vuv/7dEXwPga7Zu2aRXX3zulNtVrVa90KlirjBv+7M7lfBew0lffvmlpkyZogoVKuibb76xWi0YNWvWtH4nm/YLH374oZo2baqqVatq3rx5yszMdNvz165dO3t+z549BW6Xe13u+xRm7Nix1rncI488okqVKunYsWMuU9antM0FkqxlaWlpZ/R6AAAAAF/m5PiBsQMAAADgHAJJ8DsdOnbWo08/r+lzftOG7fu1NTpOm3Yd0ubdMfrfW++rStVqVsWVMVdfoaNxcS73NV9fO/ISHY6NUdPmLfTz/EXW/bbsPaxnJ/7XqtLy67w5evzh+x17fYA3qFipkvr07a+77r1f7334qWrUqFmk+70y8QXNnPGjNT/m5tu0ZtN27Yw+rD2H4jVv4RJ1697T+ofVow+P18oVy0r4VQC+wfzMPHTPbUpJTlaHc7oWuu2f67YXOt1+z4PZ2464+rpS2Hs45aOPPrIqQppKQpdeeqnKli2bvS4gIMD6p/91112n5cuXq169eho+fLi2bNnitudv1aqV9TzG2rVrC9wua5250FG5csHVL3Pbtm2bdfvwww9bn6Y+efrss8+s9Tt37sxe9sYbb7jhVQEAAAC+ycnxA2MHAAAAwEsCSTExMdYnFUaPHq3WrVurXLlyCg0NVd26da1BwrfffnvKx4iPj9cTTzyhdu3aWW0QIiMj1blzZ7300ktFqiqzf/9+3X///WrRooU1cDEXFnr37q1JkyadsoxrRkaGPvnkE5133nnWJy2y9v3KK6/U4sWLi3Mo4MVMW7Xb7xqnTp27KrJixezl5cqXt9a99u6H1teHDh7Q3FkzXO779muv6MD+aKui0qdTvtdZHezqDyEhIbrh5tv1wMN2q5tPP3pfUVs2lerrArxF9569FLXrgL79cZaeeOYFXXrFSKvKUVF89cWn1m3PXn008ZXXVLt2nex/Xp3doZO++OZ762fZ/D344btpJfo6AF8xedJbWv73El182Sj17jvgjB7r688nW7fndO2hxk2bu2kP4YlWrFhh3ZpxQW4nf4rZnO+//vrr1hjgP//5j9uePzw8XD179rTmZ87MvzWg+Vswa9Ysa/78889323MDAAAA8J7xA2MHAAAAwEsCSeaTxWPGjLE+FbxhwwZrwGAqwphWCN9//7316YYLL7xQiYmJ+d5/x44dat++vZ588knr08rmIoFpv7N06VI98MAD6tatmw4fPlzg8y9btkxt2rTRyy+/rE2bNikoKMganPz++++6+eabNXjw4AJDTaaVwqBBg3Tttddq7ty5OnLkiBWo2rdvn1UytlevXpo4cWJxDgd8VKdc1SH27nVtAfL1l/Yn4i++dITqN2yU575jbrnDCkOY8Nu0KV+Wwt4C3icwMPC072tatBlnd8y/FVSFyEg1bdrMmk84duy0nwfwF7t2bNfLzz2hSpWr6JGnz+yfvcv+WqItmzZa8yOuvt5NewhPZc6lTWWgirnC3WZckNV6Ibfu3btbFwHMObg7mU9QG/Pnz9eff/6ZZ/3XX3+trVu3WvNmDFBU27dvt8YpBU1Zz9ugQYPsZffee6/bXhcAAADga5wePzB2AAAAALwgkGT6OXfp0kVvvvmmoqKilJSUZAV9TFuDG2+80drm559/1q233prvfYcNG2b9g79WrVqaM2eONeAw4SUTCDIDEvNJiZM/JZElLi5OQ4cOtao0tWzZUn///bcVRjKPYT41YQYw5hPQBV0MMIElM4gxVTSee+45K/gUGxurQ4cOafz48Va4ytz+8MMPxTkk8EF/Lv49e75ho8bZ81s2/6M9u3da8/3PuyDf+5owUtfu9qf1f5vv3otuAKQGDe2fyVUrlud7OExbxS1bNhcaWgKQ41/jxioxMUH/evIFq2Xpmfj684+s24gKkbrwoks5zD6uSpUqVsuF3MzFBXNuby425Cc6Otqt+2AuKpiqqyYQdNlll2nevHnWcnNeb8JI5vzfMB9aGDDAtfqXqdhq9t9MZnwCAAAAwHfHD4wdAAAAAC8IJP3yyy/Wp49vv/12NW6cE9Ro2LCh1TItK4j06aefateuXS73nTx5stasWWPNT506VQMHDrR3ICBAI0eO1DvvvGN9PWPGjOyLCbm9+OKL1iDEtGkz25xzzjnZrbLGjh1rVV0y3n33Xat6Um7meU3oybj77rv18MMPWwEoo1KlSlb5V7MPhmkHd3KpWPg+U6nLVIn44N03dfetY6xljRo30XmDhmRv88/69dnzLVu1LvCxWrRqY91u+mdDie4z4I9uuOkW6/b3hQv04H13ZVcxMxejV61crisvv9iqjNS5azeNGHW1w3sLeLYvP/lAixbOV88+/XXpyDP7eTE/dzO+t9skDrvkCpUND3fTXsJT1alTR0ePHrU+nJClVatW2RWLclu+fLl1ocF8ytmdTLVU82ECMxYxFVvN+MJUQDXTiBEjrP3r0KGDVd0VAAAAgP+OHxg7AAAAAF4QSOrXr1+h67OqJBmmDdvJgaSsxzBlV082atQoNWpkt8D6+OOP86zPWpZ7u9zuuusuq8e0aZV18kUHE2DK8uCDD+a776Y6krFlyxarBRz8Q6MaFVS7Yqh12/WsFvr3+Pt05Mhhde7WQ199P1OhoaHZ20ZH782er1mrToGPWatWbes2/uhRWkYBbnbTrXforvsesMKsH7z3tto1b6j6NSupdpXy6t+rq7ZtjdI994/Xtz/OPqPWcICvi963Ry88+YjCypbVMy++dsaP9+N3Xyshwf7H8ojRtGvzBx07drRuTdXSLEOGDLECoqYVs1melpZmjQnMp5HNp6F79rSrSLqTCSOtXr1ajz32mNq2bWs9j6mc2qlTJ+sDDUuWLLE+gAAAAADAv8cPjB0AAAAADw8knUpYWFj2vAkGZTGfaPjjjz+yWybkxwwyBg0aZM3Pnj3bZd0///yjnTt3Fnp/E0bq3bt3vvffsWOHdRsZGanate2wyMlMG7issrEn3x++q1r1mqpWvYbCy5XLXtazd1899fyLqluvfp7qD1kKq/yQe92xY/Fu32fAn5kg0mNPPqtX33rPapGY9bOZmppqzSenJFtt2xITEhzeU8Cz/fuBuxR/NE53P/CI6jfMG/Qurimf2e3aWrVpp3Zn2f9ohm/LunhgWqNlMVVUzSefTTvnbt26WWODrl27at26ddYnkh955JES2RdT+dRUSzVVUc0nrs0nr82FDFP51FRTzY9p2Wb230zmwkRxfPTRR9b9aPUGAAAAeNf4gbEDAAAA4MWBpF9//TV7vl27dtnzGzZsyG6DZj65XJCsdaY1W2xsbPbytWvX5tmmsPuvz9VaK7fCWrGZdWZQZGS1loPv+2vNJq3atFNb9sRq9eZdeuzpF7RuzSpd2L+nJjxrtwEE4DliDh3SJUPO15233qjOXbppxpxftW3PIa2P2qXJn3+tqlWr6cNJ7+j8fj2z27kBcPXd119o/pyZat22vW68/e4zPjybNq7XymX2p1ypjuQ/LrzwQqu1wg033ODyAQHT4tlUQ80K+5ipfv36mjZtmnVxAQAAAID/YfwAAAAA+Kcgdz3QkSNH9Pzzz1vzplJRixYtstft3ZvT6sp86qEgudeZ+1SuXPm07p/Vj9pcFDGyPvUcHx9vVUtq0KBBnvvmDj3lfr5TyaqqBO9XtVp13XbXferao5eGnddH/534nDp0OkfnDRpirc+qxmIkJSYqokKFfB/HrMtSvnxEKew54D/G3jpGvy9coJ69+uib72dk/w6uEBmpoRcNV9duPdSz81navm2rnnrsX3p7kt0uFIDt0IH9eubR8VZLw2dffsP61Km7qiOFhoVp+OVXcqj9hPneOffcc/Msb9asmVUZdffu3dq1a5dVobRVq1acMwMAAAB+jPEDAAAA4J/cUiHJVBe65pprtG/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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename=str(RESULTS / 'diagnostics' / 'corrected_year_diagnostic.png')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a088bef",
   "metadata": {},
   "source": [
    "## Independent verification\n",
    "\n",
    "Recompute reported metrics from saved predictions; check training membership, source/input hashes, scalers, and the 50-epoch histories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b245738",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-20T16:41:46.209481Z",
     "iopub.status.busy": "2026-09-20T16:41:46.209392Z",
     "iopub.status.idle": "2026-09-20T16:41:48.842826Z",
     "shell.execute_reply": "2026-09-20T16:41:48.842189Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"checked_runs\": 72,\n",
      "  \"checked_neural_runs\": 36,\n",
      "  \"complete\": true,\n",
      "  \"probe_convergence_warnings\": 0,\n",
      "  \"verified\": [\n",
      "    \"feature hashes and row alignment\",\n",
      "    \"disjoint splits and recording groups\",\n",
      "    \"training-only coda membership and ICI scalers\",\n",
      "    \"50 finite-loss epochs\",\n",
      "    \"saved embeddings and checkpoints\",\n",
      "    \"prediction labels and eligibility\",\n",
      "    \"recomputed F1 and accuracy\",\n",
      "    \"validation-argmax layer choice\",\n",
      "    \"source snapshot and input provenance\"\n",
      "  ]\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "command('verify_revision_runs.py', '--require-complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2adb4e55",
   "metadata": {},
   "source": [
    "## Interpretation\n",
    "\n",
    "The original large ID-supervision gain is not reproduced. The simple ICI + temporal-mel floor is a strong ordinary-split ID baseline, and recording-date separation reduces performance for the tested audio-based models. These results support a more cautious empirical comparison, not blanket superiority of the contrastive architecture. Read `reports_v2/revision_results.md`, `revision_response_map.md`, and `revision_manuscript_changes.md` before updating the paper. Full label-free/single-channel/bandwidth ablations and external-population validation remain future work."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Paper revision V2 (.venv)",
   "language": "python",
   "name": "review_v2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}