{ "cells": [ { "cell_type": "markdown", "id": "2cd49ba5", "metadata": {}, "source": [ "# Complete review revision: V3\n", "\n", "This notebook reads the preserved experiment outputs. It does not rerun training by default. V3 is exploratory because V2 results were examined before its design. See `reports_v3/protocol.md` for the fixed experiment grid." ] }, { "cell_type": "code", "execution_count": 1, "id": "54a252af", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:04.999843Z", "iopub.status.busy": "2026-09-20T19:58:04.999635Z", "iopub.status.idle": "2026-09-20T19:58:05.291558Z", "shell.execute_reply": "2026-09-20T19:58:05.291060Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'completed_v2': 72, 'completed_v3': 114, 'expected_v3': 114, 'completed_v3_main': 114, 'completed_year_holdout': 24, 'completed_new_total': 168, 'expected_new_total': 168, 'completed_feature_fusion': 30, 'primary_evaluations_including_v2': 240, 'diagnostic_branch_probes': 24}\n" ] } ], "source": [ "from pathlib import Path\n", "import json\n", "import pandas as pd\n", "pd.set_option('display.max_rows', 120)\n", "from IPython.display import display, Markdown, Image\n", "ROOT = Path.cwd()\n", "if not (ROOT / 'results_v3').exists():\n", " ROOT = ROOT.parent\n", "assert (ROOT / 'results_v3').exists()\n", "OUT = ROOT / 'results_v3'\n", "print(json.loads((OUT / 'completion.json').read_text()))" ] }, { "cell_type": "code", "execution_count": 2, "id": "39fec52b", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.293013Z", "iopub.status.busy": "2026-09-20T19:58:05.292820Z", "iopub.status.idle": "2026-09-20T19:58:05.297971Z", "shell.execute_reply": "2026-09-20T19:58:05.297519Z" } }, "outputs": [ { "data": { "text/markdown": [ "# V3 supervision and implementation matrix\n", "\n", "U/T/I refer to labels used during representation training. Every row later freezes the representation and trains a separate supervised linear probe. All V3 samplers are uniform; auxiliary class weights use training labels only.\n", "\n", "| Model | U | T | I | Input | Pairing | Representation | Dimension |\n", "|---|---:|---:|---:|---|---|---|---:|\n", "| Dual_U0T0I0 | 0 | 0 | 0 | dual, 8 kHz | same-coda augmented views | trained, 50 epochs | 64 |\n", "| Dual_U0T0I1 | 0 | 0 | 1 | dual, 8 kHz | same-coda augmented views | trained, 50 epochs | 64 |\n", "| Dual_U0T1I0 | 0 | 1 | 0 | dual, 8 kHz | same-coda augmented views | trained, 50 epochs | 64 |\n", "| Dual_U0T1I1 | 0 | 1 | 1 | dual, 8 kHz | same-coda augmented views | trained, 50 epochs | 64 |\n", "| Dual_U1T0I0 | 1 | 0 | 0 | dual, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| Dual_U1T0I1 | 1 | 0 | 1 | dual, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| Dual_U1T1I0 | 1 | 1 | 0 | dual, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| Dual_U1T1I1 | 1 | 1 | 1 | dual, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| rhythm_UTI0 | 1 | 1 | 0 | rhythm, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| rhythm_UTI1 | 1 | 1 | 1 | rhythm, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| spectral_UTI0 | 1 | 1 | 0 | spectral, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| spectral_UTI1 | 1 | 1 | 1 | spectral, 8 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| concat_UTI0 | 1 | 1 | 0 | concat, 8 kHz | same-unit whole codas | trained, 50 epochs | 128 |\n", "| concat_UTI1 | 1 | 1 | 1 | concat, 8 kHz | same-unit whole codas | trained, 50 epochs | 128 |\n", "| Dual_swap_UTI | 1 | 1 | 1 | dual, 8 kHz | same-unit swapped channels | trained, 50 epochs | 64 |\n", "| Dual_16k_UTI0 | 1 | 1 | 0 | dual, 16 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| Dual_16k_UTI1 | 1 | 1 | 1 | dual, 16 kHz | same-unit whole codas | trained, 50 epochs | 64 |\n", "| AVES_FT | CE | CE | CE | audio | none | transformer fine-tuned, 10 epochs; frontend frozen | 64 |\n", "| AVES_ICI_FT | CE | CE | CE | audio + ICI | none | transformer fine-tuned, 10 epochs; frontend frozen | 64 |\n", "\n", "Single-channel auxiliary heads both use the available channel. Dual/concatenation type heads use rhythm and ID heads use spectrum. U=0 removes unit labels from representation training but not from evaluation partitioning/probes. Label-free objectives still use annotation-derived ICI inputs. AVES FT has a different pretraining history, parameter count and epoch budget from DCCE.\n", "\n", "## V2 benchmark: downstream whale-label access\n", "\n", "These columns describe local downstream training, not all labels used in external pretraining. Every row uses a subsequently trained supervised probe.\n", "\n", "| Model | Unit use | Type loss | ID loss | Unit-balanced sampler | Representation | Input |\n", "|---|---|---:|---:|---:|---|---|\n", "| Raw ICI | none | no | no | no | fixed features | annotated ICI |\n", "| Temporal mel | none | no | no | no | fixed features | audio |\n", "| ICI + temporal mel | none | no | no | no | fixed features | annotated ICI + audio |\n", "| WhAM | none | no | no | no | frozen pretrained; WhAM/AVES layer selected on validation | audio |\n", "| AVES | none | no | no | no | frozen pretrained; WhAM/AVES layer selected on validation | audio |\n", "| BirdNET | none | no | no | no | frozen pretrained; WhAM/AVES layer selected on validation | audio |\n", "| DCCE no ID | whole-coda unit pairing | yes | no | yes | trained, 50 epochs | annotated ICI + audio |\n", "| DCCE ID | whole-coda unit pairing | yes | yes | yes | trained, 50 epochs | annotated ICI + audio |\n", "| DCCE swap ID | same-unit swapped channels | yes | yes | yes | trained, 50 epochs | annotated ICI + audio |\n", "| Supervised dual | unit CE | yes | yes | yes | trained head/fusion; AVES backbone frozen in adapter rows | annotated ICI + audio |\n", "| AVES adapter | unit CE | yes | yes | yes | trained head/fusion; AVES backbone frozen in adapter rows | audio |\n", "| AVES + ICI adapter | unit CE | yes | yes | yes | trained head/fusion; AVES backbone frozen in adapter rows | annotated ICI + audio |\n", "\n", "## Additional frozen input-matched controls\n", "\n", "WhAM + ICI, AVES + ICI, and BirdNET + ICI concatenate annotated timing with frozen audio embeddings. They use no local representation-training labels, losses, pairing, or sampler. Only the training-only scaler and supervised linear probe are fitted; WhAM/AVES layers are selected on validation data. External pretraining label/data access remains as described above. These controls are evaluated on all six random/date partitions and all four year holdouts.\n", "\n", "The strict year diagnostic reuses the same definitions for Raw ICI, ICI + temporal mel, WhAM, AVES, DCCE 110 and DCCE 111. The latter two are trained afresh on each year partition with uniform sampling. Saved-branch probes are post-hoc diagnostics on fixed DCCE 110/111 checkpoints and do not replace the primary fused-representation results." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Markdown((ROOT / 'reports_v3/supervision_matrix.md').read_text()))" ] }, { "cell_type": "code", "execution_count": 3, "id": "5372ca6b", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.299214Z", "iopub.status.busy": "2026-09-20T19:58:05.299091Z", "iopub.status.idle": "2026-09-20T19:58:05.328432Z", "shell.execute_reply": "2026-09-20T19:58:05.327736Z" } }, "outputs": [ { "data": { "text/html": [ "
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countmeanstd
taskcoda_typeindividual_idunitcoda_typeindividual_idunitcoda_typeindividual_idunit
versionprotocolmodel
v2date_groupedAVES3.03.03.00.0613770.0738390.6996060.0222730.0510100.099810
AVES_Adapter3.03.03.00.0788890.0587770.6463270.0287650.0438980.134228
AVES_ICI_Adapter3.03.03.00.1832200.0811830.6686030.0371600.0723430.118781
BirdNET3.03.03.00.0625220.0849210.6117270.0107180.0217970.133217
DCCE_ID3.03.03.00.5023190.2572500.5738930.0175920.0627910.074437
DCCE_no_ID3.03.03.00.6157270.2811700.6865730.0655190.1085230.023382
DCCE_swap_ID3.03.03.00.4759780.2192520.6111100.0840460.0888750.014209
ICI_Temporal_Mel3.03.03.00.6675750.2553340.6075840.0508410.0615280.149813
Raw_ICI3.03.03.00.9564080.3790350.5396290.0362220.1029400.035826
Supervised_Dual3.03.03.00.6754030.2697410.6114730.0578310.0954720.079680
Temporal_Mel3.03.03.00.0689070.1296340.5689080.0116730.0740120.094056
WhAM3.03.03.00.0659560.1007250.6666660.0163080.0745190.155343
randomAVES3.03.03.00.2526010.4911310.9097810.0275280.0294070.009811
AVES_Adapter3.03.03.00.2715760.4785300.9325440.0271860.0183120.009488
AVES_ICI_Adapter3.03.03.00.4515310.5820730.9399950.0230570.0780580.009122
BirdNET3.03.03.00.2154460.4803560.8873350.0526970.0219910.019882
DCCE_ID3.03.03.00.7219580.5880050.8893660.0513360.0793610.004248
DCCE_no_ID3.03.03.00.7999520.6269330.9068460.0263070.0569970.002644
DCCE_swap_ID3.03.03.00.6364230.6407990.8792310.0423600.0657710.028993
ICI_Temporal_Mel3.03.03.00.8042180.7237830.8374260.0156890.0390930.012893
Raw_ICI3.03.03.00.9874530.4390540.5703000.0086720.0070790.023350
Supervised_Dual3.03.03.00.7617450.6838310.8858320.1192110.0808520.016999
Temporal_Mel3.03.03.00.2017530.4292380.7725090.0249820.0449840.006913
WhAM3.03.03.00.2581390.4497910.9058120.0456380.0518870.011270
v3date_groupedAVES_FT3.03.03.00.0716060.0596930.6264290.0504730.0262420.157076
AVES_ICI_FT3.03.03.00.2107300.0726740.6409720.0689200.0383550.157738
Dual_16k_UTI03.03.03.00.7082790.2499940.6240670.1468850.1313080.003592
Dual_16k_UTI13.03.03.00.6694890.1729090.6118940.0112720.1415280.055992
Dual_U0T0I03.03.03.00.4689950.1814330.5416500.0426150.1331700.090488
Dual_U0T0I13.03.03.00.4410140.1589940.4847590.0559720.0543530.101975
Dual_U0T1I03.03.03.00.6622800.2088780.5512680.0806950.0908730.078475
Dual_U0T1I13.03.03.00.6793370.1738830.5514260.0369790.0683780.113046
Dual_U1T0I03.03.03.00.3480140.2447190.5177870.0392660.0842090.139986
Dual_U1T0I13.03.03.00.2701150.1338990.5112060.0109460.0385320.117560
Dual_U1T1I03.03.03.00.6920720.2481220.5975020.0882800.1299350.077931
Dual_U1T1I13.03.03.00.6276680.2231430.5959970.0744290.1751190.052135
Dual_swap_UTI3.03.03.00.4711340.1892780.6212510.0985480.0694110.070424
concat_UTI03.03.03.00.8593370.2753780.5491330.0144560.1165700.171126
concat_UTI13.03.03.00.8496760.2391120.5272210.0827560.0429400.140180
rhythm_UTI03.03.03.00.9270800.3522970.4865650.0467690.1336900.090828
rhythm_UTI13.03.03.00.9189030.3306710.5051410.0447460.1431140.113215
spectral_UTI03.03.03.00.0620410.0536080.5405880.0252250.0469020.129682
spectral_UTI13.03.03.00.0628500.0917670.5585690.0110010.0161340.093290
randomAVES_FT3.03.03.00.2431970.4140240.9239690.0254320.0780940.006931
AVES_ICI_FT3.03.03.00.4661300.5438990.9127000.0588630.0169690.010566
Dual_16k_UTI03.03.03.00.9112010.6309820.9014710.0571150.0461880.006359
Dual_16k_UTI13.03.03.00.7274620.6230310.8931160.0881850.0457120.007781
Dual_U0T0I03.03.03.00.5826620.6239580.6932500.0419340.0644560.026777
Dual_U0T0I13.03.03.00.5657260.5924380.6736750.0168130.0612730.015982
Dual_U0T1I03.03.03.00.7698360.5914710.7094900.0857690.0572320.026989
Dual_U0T1I13.03.03.00.8015210.6001530.7253600.0456550.0801430.013393
Dual_U1T0I03.03.03.00.4926730.4341260.8611460.0906450.0667910.001385
Dual_U1T0I13.03.03.00.3483010.4740680.8850480.1116850.0315560.018935
Dual_U1T1I03.03.03.00.8654000.5970340.9009590.1147350.0510780.020301
Dual_U1T1I13.03.03.00.7353360.5808330.9073710.0905260.0119020.010447
Dual_swap_UTI3.03.03.00.7083290.6571220.8538370.0731610.0666120.012703
concat_UTI03.03.03.00.9387460.6357400.8757690.0620730.0153600.024578
concat_UTI13.03.03.00.9268690.6956420.8728770.0599220.0546080.010124
rhythm_UTI03.03.03.00.9783180.4774650.5812790.0208820.0367410.043923
rhythm_UTI13.03.03.00.9731650.4541290.6042560.0128800.0605160.052289
spectral_UTI03.03.03.00.2020730.3577160.8752580.0212760.1032990.022978
spectral_UTI13.03.03.00.1990690.4444000.8536820.0370310.0921840.006178
\n", "
" ], "text/plain": [ " count mean \\\n", "task coda_type individual_id unit coda_type \n", "version protocol model \n", "v2 date_grouped AVES 3.0 3.0 3.0 0.061377 \n", " AVES_Adapter 3.0 3.0 3.0 0.078889 \n", " AVES_ICI_Adapter 3.0 3.0 3.0 0.183220 \n", " BirdNET 3.0 3.0 3.0 0.062522 \n", " DCCE_ID 3.0 3.0 3.0 0.502319 \n", " DCCE_no_ID 3.0 3.0 3.0 0.615727 \n", " DCCE_swap_ID 3.0 3.0 3.0 0.475978 \n", " ICI_Temporal_Mel 3.0 3.0 3.0 0.667575 \n", " Raw_ICI 3.0 3.0 3.0 0.956408 \n", " Supervised_Dual 3.0 3.0 3.0 0.675403 \n", " Temporal_Mel 3.0 3.0 3.0 0.068907 \n", " WhAM 3.0 3.0 3.0 0.065956 \n", " random AVES 3.0 3.0 3.0 0.252601 \n", " AVES_Adapter 3.0 3.0 3.0 0.271576 \n", " AVES_ICI_Adapter 3.0 3.0 3.0 0.451531 \n", " BirdNET 3.0 3.0 3.0 0.215446 \n", " DCCE_ID 3.0 3.0 3.0 0.721958 \n", " DCCE_no_ID 3.0 3.0 3.0 0.799952 \n", " DCCE_swap_ID 3.0 3.0 3.0 0.636423 \n", " ICI_Temporal_Mel 3.0 3.0 3.0 0.804218 \n", " Raw_ICI 3.0 3.0 3.0 0.987453 \n", " Supervised_Dual 3.0 3.0 3.0 0.761745 \n", " Temporal_Mel 3.0 3.0 3.0 0.201753 \n", " WhAM 3.0 3.0 3.0 0.258139 \n", "v3 date_grouped AVES_FT 3.0 3.0 3.0 0.071606 \n", " AVES_ICI_FT 3.0 3.0 3.0 0.210730 \n", " Dual_16k_UTI0 3.0 3.0 3.0 0.708279 \n", " Dual_16k_UTI1 3.0 3.0 3.0 0.669489 \n", " Dual_U0T0I0 3.0 3.0 3.0 0.468995 \n", " Dual_U0T0I1 3.0 3.0 3.0 0.441014 \n", " Dual_U0T1I0 3.0 3.0 3.0 0.662280 \n", " Dual_U0T1I1 3.0 3.0 3.0 0.679337 \n", " Dual_U1T0I0 3.0 3.0 3.0 0.348014 \n", " Dual_U1T0I1 3.0 3.0 3.0 0.270115 \n", " Dual_U1T1I0 3.0 3.0 3.0 0.692072 \n", " Dual_U1T1I1 3.0 3.0 3.0 0.627668 \n", " Dual_swap_UTI 3.0 3.0 3.0 0.471134 \n", " concat_UTI0 3.0 3.0 3.0 0.859337 \n", " concat_UTI1 3.0 3.0 3.0 0.849676 \n", " rhythm_UTI0 3.0 3.0 3.0 0.927080 \n", " rhythm_UTI1 3.0 3.0 3.0 0.918903 \n", " spectral_UTI0 3.0 3.0 3.0 0.062041 \n", " spectral_UTI1 3.0 3.0 3.0 0.062850 \n", " random AVES_FT 3.0 3.0 3.0 0.243197 \n", " AVES_ICI_FT 3.0 3.0 3.0 0.466130 \n", " Dual_16k_UTI0 3.0 3.0 3.0 0.911201 \n", " Dual_16k_UTI1 3.0 3.0 3.0 0.727462 \n", " Dual_U0T0I0 3.0 3.0 3.0 0.582662 \n", " Dual_U0T0I1 3.0 3.0 3.0 0.565726 \n", " Dual_U0T1I0 3.0 3.0 3.0 0.769836 \n", " Dual_U0T1I1 3.0 3.0 3.0 0.801521 \n", " Dual_U1T0I0 3.0 3.0 3.0 0.492673 \n", " Dual_U1T0I1 3.0 3.0 3.0 0.348301 \n", " Dual_U1T1I0 3.0 3.0 3.0 0.865400 \n", " Dual_U1T1I1 3.0 3.0 3.0 0.735336 \n", " Dual_swap_UTI 3.0 3.0 3.0 0.708329 \n", " concat_UTI0 3.0 3.0 3.0 0.938746 \n", " concat_UTI1 3.0 3.0 3.0 0.926869 \n", " rhythm_UTI0 3.0 3.0 3.0 0.978318 \n", " rhythm_UTI1 3.0 3.0 3.0 0.973165 \n", " spectral_UTI0 3.0 3.0 3.0 0.202073 \n", " spectral_UTI1 3.0 3.0 3.0 0.199069 \n", "\n", " std \\\n", "task individual_id unit coda_type \n", "version protocol model \n", "v2 date_grouped AVES 0.073839 0.699606 0.022273 \n", " AVES_Adapter 0.058777 0.646327 0.028765 \n", " AVES_ICI_Adapter 0.081183 0.668603 0.037160 \n", " BirdNET 0.084921 0.611727 0.010718 \n", " DCCE_ID 0.257250 0.573893 0.017592 \n", " DCCE_no_ID 0.281170 0.686573 0.065519 \n", " DCCE_swap_ID 0.219252 0.611110 0.084046 \n", " ICI_Temporal_Mel 0.255334 0.607584 0.050841 \n", " Raw_ICI 0.379035 0.539629 0.036222 \n", " Supervised_Dual 0.269741 0.611473 0.057831 \n", " Temporal_Mel 0.129634 0.568908 0.011673 \n", " WhAM 0.100725 0.666666 0.016308 \n", " random AVES 0.491131 0.909781 0.027528 \n", " AVES_Adapter 0.478530 0.932544 0.027186 \n", " AVES_ICI_Adapter 0.582073 0.939995 0.023057 \n", " BirdNET 0.480356 0.887335 0.052697 \n", " DCCE_ID 0.588005 0.889366 0.051336 \n", " DCCE_no_ID 0.626933 0.906846 0.026307 \n", " DCCE_swap_ID 0.640799 0.879231 0.042360 \n", " ICI_Temporal_Mel 0.723783 0.837426 0.015689 \n", " Raw_ICI 0.439054 0.570300 0.008672 \n", " Supervised_Dual 0.683831 0.885832 0.119211 \n", " Temporal_Mel 0.429238 0.772509 0.024982 \n", " WhAM 0.449791 0.905812 0.045638 \n", "v3 date_grouped AVES_FT 0.059693 0.626429 0.050473 \n", " AVES_ICI_FT 0.072674 0.640972 0.068920 \n", " Dual_16k_UTI0 0.249994 0.624067 0.146885 \n", " Dual_16k_UTI1 0.172909 0.611894 0.011272 \n", " Dual_U0T0I0 0.181433 0.541650 0.042615 \n", " Dual_U0T0I1 0.158994 0.484759 0.055972 \n", " Dual_U0T1I0 0.208878 0.551268 0.080695 \n", " Dual_U0T1I1 0.173883 0.551426 0.036979 \n", " Dual_U1T0I0 0.244719 0.517787 0.039266 \n", " Dual_U1T0I1 0.133899 0.511206 0.010946 \n", " Dual_U1T1I0 0.248122 0.597502 0.088280 \n", " Dual_U1T1I1 0.223143 0.595997 0.074429 \n", " Dual_swap_UTI 0.189278 0.621251 0.098548 \n", " concat_UTI0 0.275378 0.549133 0.014456 \n", " concat_UTI1 0.239112 0.527221 0.082756 \n", " rhythm_UTI0 0.352297 0.486565 0.046769 \n", " rhythm_UTI1 0.330671 0.505141 0.044746 \n", " spectral_UTI0 0.053608 0.540588 0.025225 \n", " spectral_UTI1 0.091767 0.558569 0.011001 \n", " random AVES_FT 0.414024 0.923969 0.025432 \n", " AVES_ICI_FT 0.543899 0.912700 0.058863 \n", " Dual_16k_UTI0 0.630982 0.901471 0.057115 \n", " Dual_16k_UTI1 0.623031 0.893116 0.088185 \n", " Dual_U0T0I0 0.623958 0.693250 0.041934 \n", " Dual_U0T0I1 0.592438 0.673675 0.016813 \n", " Dual_U0T1I0 0.591471 0.709490 0.085769 \n", " Dual_U0T1I1 0.600153 0.725360 0.045655 \n", " Dual_U1T0I0 0.434126 0.861146 0.090645 \n", " Dual_U1T0I1 0.474068 0.885048 0.111685 \n", " Dual_U1T1I0 0.597034 0.900959 0.114735 \n", " Dual_U1T1I1 0.580833 0.907371 0.090526 \n", " Dual_swap_UTI 0.657122 0.853837 0.073161 \n", " concat_UTI0 0.635740 0.875769 0.062073 \n", " concat_UTI1 0.695642 0.872877 0.059922 \n", " rhythm_UTI0 0.477465 0.581279 0.020882 \n", " rhythm_UTI1 0.454129 0.604256 0.012880 \n", " spectral_UTI0 0.357716 0.875258 0.021276 \n", " spectral_UTI1 0.444400 0.853682 0.037031 \n", "\n", " \n", "task individual_id unit \n", "version protocol model \n", "v2 date_grouped AVES 0.051010 0.099810 \n", " AVES_Adapter 0.043898 0.134228 \n", " AVES_ICI_Adapter 0.072343 0.118781 \n", " BirdNET 0.021797 0.133217 \n", " DCCE_ID 0.062791 0.074437 \n", " DCCE_no_ID 0.108523 0.023382 \n", " DCCE_swap_ID 0.088875 0.014209 \n", " ICI_Temporal_Mel 0.061528 0.149813 \n", " Raw_ICI 0.102940 0.035826 \n", " Supervised_Dual 0.095472 0.079680 \n", " Temporal_Mel 0.074012 0.094056 \n", " WhAM 0.074519 0.155343 \n", " random AVES 0.029407 0.009811 \n", " AVES_Adapter 0.018312 0.009488 \n", " AVES_ICI_Adapter 0.078058 0.009122 \n", " BirdNET 0.021991 0.019882 \n", " DCCE_ID 0.079361 0.004248 \n", " DCCE_no_ID 0.056997 0.002644 \n", " DCCE_swap_ID 0.065771 0.028993 \n", " ICI_Temporal_Mel 0.039093 0.012893 \n", " Raw_ICI 0.007079 0.023350 \n", " Supervised_Dual 0.080852 0.016999 \n", " Temporal_Mel 0.044984 0.006913 \n", " WhAM 0.051887 0.011270 \n", "v3 date_grouped AVES_FT 0.026242 0.157076 \n", " AVES_ICI_FT 0.038355 0.157738 \n", " Dual_16k_UTI0 0.131308 0.003592 \n", " Dual_16k_UTI1 0.141528 0.055992 \n", " Dual_U0T0I0 0.133170 0.090488 \n", " Dual_U0T0I1 0.054353 0.101975 \n", " Dual_U0T1I0 0.090873 0.078475 \n", " Dual_U0T1I1 0.068378 0.113046 \n", " Dual_U1T0I0 0.084209 0.139986 \n", " Dual_U1T0I1 0.038532 0.117560 \n", " Dual_U1T1I0 0.129935 0.077931 \n", " Dual_U1T1I1 0.175119 0.052135 \n", " Dual_swap_UTI 0.069411 0.070424 \n", " concat_UTI0 0.116570 0.171126 \n", " concat_UTI1 0.042940 0.140180 \n", " rhythm_UTI0 0.133690 0.090828 \n", " rhythm_UTI1 0.143114 0.113215 \n", " spectral_UTI0 0.046902 0.129682 \n", " spectral_UTI1 0.016134 0.093290 \n", " random AVES_FT 0.078094 0.006931 \n", " AVES_ICI_FT 0.016969 0.010566 \n", " Dual_16k_UTI0 0.046188 0.006359 \n", " Dual_16k_UTI1 0.045712 0.007781 \n", " Dual_U0T0I0 0.064456 0.026777 \n", " Dual_U0T0I1 0.061273 0.015982 \n", " Dual_U0T1I0 0.057232 0.026989 \n", " Dual_U0T1I1 0.080143 0.013393 \n", " Dual_U1T0I0 0.066791 0.001385 \n", " Dual_U1T0I1 0.031556 0.018935 \n", " Dual_U1T1I0 0.051078 0.020301 \n", " Dual_U1T1I1 0.011902 0.010447 \n", " Dual_swap_UTI 0.066612 0.012703 \n", " concat_UTI0 0.015360 0.024578 \n", " concat_UTI1 0.054608 0.010124 \n", " rhythm_UTI0 0.036741 0.043923 \n", " rhythm_UTI1 0.060516 0.052289 \n", " spectral_UTI0 0.103299 0.022978 \n", " spectral_UTI1 0.092184 0.006178 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary = pd.read_csv(OUT / 'summary.csv')\n", "display(summary.pivot_table(index=['version','protocol','model'], columns='task', values=['mean','std','count']))" ] }, { "cell_type": "markdown", "id": "7c6d4c2c", "metadata": {}, "source": [ "## Conditional uncertainty\n", "\n", "Date-cluster bootstrap confidence intervals condition on each fitted model. They do not capture training variability. Intervals are not estimated for fewer than two supported test dates. Classes absent from a bootstrap resample score zero under the fixed original class set. Three-run SD and per-run intervals answer different questions. Full intervals are in `results_v3/cluster_bootstrap.csv`; secondary scores including unseen classes are in `results_v3/secondary_summary_including_unseen.csv`." ] }, { "cell_type": "code", "execution_count": 4, "id": "338f5802", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.329815Z", "iopub.status.busy": "2026-09-20T19:58:05.329680Z", "iopub.status.idle": "2026-09-20T19:58:05.338049Z", "shell.execute_reply": "2026-09-20T19:58:05.337432Z" } }, "outputs": [ { "data": { "text/html": [ "
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modelprotocolseedversiontaskloweruppern_datesn_test
0AVESdate_grouped0v2unit0.2820670.6204505328
1AVESdate_grouped0v2coda_type0.0071230.0476415326
2AVESdate_grouped0v2individual_idNaNNaN1186
3AVES_Adapterdate_grouped0v2unit0.2357570.5480725328
4AVES_Adapterdate_grouped0v2coda_type0.0114960.0578915326
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553spectral_UTI0random2v3coda_type0.1012630.22434140277
554spectral_UTI0random2v3individual_id0.1388960.34028923151
555spectral_UTI1random2v3unit0.7461050.90412040277
556spectral_UTI1random2v3coda_type0.0974610.19716440277
557spectral_UTI1random2v3individual_id0.1636040.44967823151
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558 rows × 9 columns

\n", "
" ], "text/plain": [ " model protocol seed version task lower \\\n", "0 AVES date_grouped 0 v2 unit 0.282067 \n", "1 AVES date_grouped 0 v2 coda_type 0.007123 \n", "2 AVES date_grouped 0 v2 individual_id NaN \n", "3 AVES_Adapter date_grouped 0 v2 unit 0.235757 \n", "4 AVES_Adapter date_grouped 0 v2 coda_type 0.011496 \n", ".. ... ... ... ... ... ... \n", "553 spectral_UTI0 random 2 v3 coda_type 0.101263 \n", "554 spectral_UTI0 random 2 v3 individual_id 0.138896 \n", "555 spectral_UTI1 random 2 v3 unit 0.746105 \n", "556 spectral_UTI1 random 2 v3 coda_type 0.097461 \n", "557 spectral_UTI1 random 2 v3 individual_id 0.163604 \n", "\n", " upper n_dates n_test \n", "0 0.620450 5 328 \n", "1 0.047641 5 326 \n", "2 NaN 1 186 \n", "3 0.548072 5 328 \n", "4 0.057891 5 326 \n", ".. ... ... ... \n", "553 0.224341 40 277 \n", "554 0.340289 23 151 \n", "555 0.904120 40 277 \n", "556 0.197164 40 277 \n", "557 0.449678 23 151 \n", "\n", "[558 rows x 9 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intervals = pd.read_csv(OUT / 'cluster_bootstrap.csv')\n", "display(intervals)" ] }, { "cell_type": "code", "execution_count": 5, "id": "d7a9c6a0", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.339357Z", "iopub.status.busy": "2026-09-20T19:58:05.339234Z", "iopub.status.idle": "2026-09-20T19:58:05.348809Z", "shell.execute_reply": "2026-09-20T19:58:05.348334Z" } }, "outputs": [ { "data": { "text/html": [ "
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modelprotocolseedversionunitn_trainn_testn_train_idsn_test_idsmacro_f1selected_layern_validationvalidation_macro_f1probe_convergence_warning
0AVESdate_grouped0v2A0000NaNNaNNaNNaNNaN
1AVESdate_grouped0v2D10020NaNNaNNaNNaNNaN
2AVESdate_grouped0v2F420186730.01960810.0111.00.257803False
3AVES_Adapterdate_grouped0v2A0000NaNNaNNaNNaNNaN
4AVES_Adapterdate_grouped0v2D10020NaNNaNNaNNaNNaN
.............................................
553spectral_UTI0random2v3D62220.3333330.02.01.000000False
554spectral_UTI0random2v3F458144770.3437380.0115.00.379803False
555spectral_UTI1random2v3A185220.5833330.04.00.733333False
556spectral_UTI1random2v3D62220.3333330.02.00.000000False
557spectral_UTI1random2v3F458144770.3707120.0115.00.411015False
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558 rows × 14 columns

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" ], "text/plain": [ " model protocol seed version unit n_train n_test \\\n", "0 AVES date_grouped 0 v2 A 0 0 \n", "1 AVES date_grouped 0 v2 D 10 0 \n", "2 AVES date_grouped 0 v2 F 420 186 \n", "3 AVES_Adapter date_grouped 0 v2 A 0 0 \n", "4 AVES_Adapter date_grouped 0 v2 D 10 0 \n", ".. ... ... ... ... ... ... ... \n", "553 spectral_UTI0 random 2 v3 D 6 2 \n", "554 spectral_UTI0 random 2 v3 F 458 144 \n", "555 spectral_UTI1 random 2 v3 A 18 5 \n", "556 spectral_UTI1 random 2 v3 D 6 2 \n", "557 spectral_UTI1 random 2 v3 F 458 144 \n", "\n", " n_train_ids n_test_ids macro_f1 selected_layer n_validation \\\n", "0 0 0 NaN NaN NaN \n", "1 2 0 NaN NaN NaN \n", "2 7 3 0.019608 10.0 111.0 \n", "3 0 0 NaN NaN NaN \n", "4 2 0 NaN NaN NaN \n", ".. ... ... ... ... ... \n", "553 2 2 0.333333 0.0 2.0 \n", "554 7 7 0.343738 0.0 115.0 \n", "555 2 2 0.583333 0.0 4.0 \n", "556 2 2 0.333333 0.0 2.0 \n", "557 7 7 0.370712 0.0 115.0 \n", "\n", " validation_macro_f1 probe_convergence_warning \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 0.257803 False \n", "3 NaN NaN \n", "4 NaN NaN \n", ".. ... ... \n", "553 1.000000 False \n", "554 0.379803 False \n", "555 0.733333 False \n", "556 0.000000 False \n", "557 0.411015 False \n", "\n", "[558 rows x 14 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Blank scores indicate fewer than two supported train/test identities.\n" ] } ], "source": [ "within = pd.read_csv(OUT / 'within_unit_id.csv')\n", "display(within)\n", "print('Blank scores indicate fewer than two supported train/test identities.')" ] }, { "cell_type": "code", "execution_count": 6, "id": "601ad65f", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.350202Z", "iopub.status.busy": "2026-09-20T19:58:05.350079Z", "iopub.status.idle": "2026-09-20T19:58:05.356513Z", "shell.execute_reply": "2026-09-20T19:58:05.355975Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for protocol in ['random','date_grouped']:\n", " figure = OUT / 'figures' / f'{protocol}.png'\n", " if figure.exists():\n", " display(Image(filename=str(figure)))" ] }, { "cell_type": "code", "execution_count": 7, "id": "c4b2106f", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.357717Z", "iopub.status.busy": "2026-09-20T19:58:05.357593Z", "iopub.status.idle": "2026-09-20T19:58:05.361838Z", "shell.execute_reply": "2026-09-20T19:58:05.361381Z" } }, "outputs": [ { "data": { "text/plain": [ "{'checked': 114,\n", " 'expected': 114,\n", " 'missing': [],\n", " 'all_complete': True,\n", " 'checks': 'source and split hashes; train/test separation; train-only scaler; uniform sampler metadata; fixed epochs; finite losses and embeddings; supported labels and recomputed scores; probe convergence'}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'checked': 516, 'changed': []}" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "verification = OUT / 'verification.json'\n", "if verification.exists():\n", " display(json.loads(verification.read_text()))\n", "else:\n", " print('Full verification is pending completion of the experiment grid.')\n", "preserved = ROOT / 'registry_v3/v2_preservation.json'\n", "display(json.loads(preserved.read_text()))" ] }, { "cell_type": "code", "execution_count": 8, "id": "fc71f137", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.363051Z", "iopub.status.busy": "2026-09-20T19:58:05.362947Z", "iopub.status.idle": "2026-09-20T19:58:05.367005Z", "shell.execute_reply": "2026-09-20T19:58:05.366545Z" } }, "outputs": [ { "data": { "text/markdown": [ "# Explicit year holdout diagnostic\n", "\n", "This exploratory amendment was specified after partial V3 random-split results were visible, before any year-holdout execution. Each year is held out completely; training and validation use disjoint dates from other years. All six prespecified models and all four years are reported. Neural seed is fixed at 0, so these are four different populations, not four repeat-seed estimates.\n", "\n", "## Partition sizes\n", "\n", "| Held-out year | Train codas | Validation codas | Test codas |\n", "|---|---:|---:|---:|\n", "| 2005 | 687 | 328 | 368 |\n", "| 2008 | 845 | 275 | 263 |\n", "| 2009 | 967 | 321 | 95 |\n", "| 2010 | 570 | 156 | 657 |\n", "\n", "## unit\n", "\n", "| Model | 2005 | 2008 | 2009 | 2010 |\n", "|---|---:|---:|---:|---:|\n", "| Raw ICI | 0.224 (368/368 supported) | 0.425 (263/263 supported) | 0.541 (95/95 supported) | 0.476 (657/657 supported) |\n", "| ICI + temporal mel | 0.649 (368/368 supported) | 0.360 (263/263 supported) | 0.778 (95/95 supported) | 0.327 (657/657 supported) |\n", "| WhAM | 0.822 (368/368 supported) | 0.371 (263/263 supported) | 0.789 (95/95 supported) | 0.422 (657/657 supported) |\n", "| AVES | 0.750 (368/368 supported) | 0.510 (263/263 supported) | 0.833 (95/95 supported) | 0.377 (657/657 supported) |\n", "| DCCE 110 | 0.592 (368/368 supported) | 0.362 (263/263 supported) | 0.767 (95/95 supported) | 0.346 (657/657 supported) |\n", "| DCCE 111 | 0.448 (368/368 supported) | 0.391 (263/263 supported) | 0.713 (95/95 supported) | 0.357 (657/657 supported) |\n", "\n", "## coda_type\n", "\n", "| Model | 2005 | 2008 | 2009 | 2010 |\n", "|---|---:|---:|---:|---:|\n", "| Raw ICI | 0.957 (358/368 supported) | 0.908 (263/263 supported) | 1.000 (95/95 supported) | 0.965 (636/657 supported) |\n", "| ICI + temporal mel | 0.589 (358/368 supported) | 0.607 (263/263 supported) | 0.977 (95/95 supported) | 0.427 (636/657 supported) |\n", "| WhAM | 0.077 (358/368 supported) | 0.045 (263/263 supported) | 0.166 (95/95 supported) | 0.051 (636/657 supported) |\n", "| AVES | 0.065 (358/368 supported) | 0.047 (263/263 supported) | 0.103 (95/95 supported) | 0.029 (636/657 supported) |\n", "| DCCE 110 | 0.596 (358/368 supported) | 0.702 (263/263 supported) | 1.000 (95/95 supported) | 0.476 (636/657 supported) |\n", "| DCCE 111 | 0.583 (358/368 supported) | 0.546 (263/263 supported) | 0.839 (95/95 supported) | 0.421 (636/657 supported) |\n", "\n", "## individual_id\n", "\n", "| Model | 2005 | 2008 | 2009 | 2010 |\n", "|---|---:|---:|---:|---:|\n", "| Raw ICI | 0.404 (236/314 supported) | 0.542 (57/57 supported) | 0.412 (37/64 supported) | 0.232 (292/319 supported) |\n", "| ICI + temporal mel | 0.353 (236/314 supported) | 0.491 (57/57 supported) | 0.410 (37/64 supported) | 0.128 (292/319 supported) |\n", "| WhAM | 0.238 (236/314 supported) | 0.229 (57/57 supported) | 0.038 (37/64 supported) | 0.093 (292/319 supported) |\n", "| AVES | 0.193 (236/314 supported) | 0.207 (57/57 supported) | 0.228 (37/64 supported) | 0.068 (292/319 supported) |\n", "| DCCE 110 | 0.258 (236/314 supported) | 0.607 (57/57 supported) | 0.468 (37/64 supported) | 0.197 (292/319 supported) |\n", "| DCCE 111 | 0.257 (236/314 supported) | 0.670 (57/57 supported) | 0.349 (37/64 supported) | 0.186 (292/319 supported) |\n", "\n", "Full-eligible macro-F1 values (including unseen labels) are in `year_holdout_metrics.csv`. Conditional date-cluster intervals are in `year_holdout_intervals.csv`. Class support and composition differ across years. These tests remove shared years between train/validation and test, but performance differences still combine biological, recording and label-composition changes; they do not identify a causal recording artifact." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'checked': 24, 'expected': 24, 'all_complete': True}" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "year_report = ROOT / 'reports_v3/year_holdout.md'\n", "if year_report.exists():\n", " display(Markdown(year_report.read_text()))\n", " display(json.loads((OUT / 'year_holdout_verification.json').read_text()))\n", "else:\n", " print('Year-holdout diagnostic is queued after the main grid.')" ] }, { "cell_type": "code", "execution_count": 9, "id": "29a30428", "metadata": { "execution": { "iopub.execute_input": "2026-09-20T19:58:05.368122Z", "iopub.status.busy": "2026-09-20T19:58:05.368015Z", "iopub.status.idle": "2026-09-20T19:58:05.371896Z", "shell.execute_reply": "2026-09-20T19:58:05.371375Z" } }, "outputs": [ { "data": { "text/markdown": [ "# Input-matched frozen-feature controls\n", "\n", "Exploratory addition: all three frozen pretrained families receive the same annotated ICI through direct concatenation. Only the downstream linear probe is trained. Multi-layer selection uses validation data exclusively. These controls match available inputs; they do not match external pretraining history or local auxiliary-label objectives.\n", "\n", "## date_grouped\n", "\n", "| Model | Unit | Type | ID |\n", "|---|---:|---:|---:|\n", "| AVES_ICI | 0.712 ± 0.111 | 0.366 ± 0.084 | 0.128 ± 0.103 |\n", "| BirdNET_ICI | 0.628 ± 0.095 | 0.287 ± 0.061 | 0.100 ± 0.055 |\n", "| WhAM_ICI | 0.768 ± 0.057 | 0.376 ± 0.151 | 0.153 ± 0.135 |\n", "\n", "## random\n", "\n", "| Model | Unit | Type | ID |\n", "|---|---:|---:|---:|\n", "| AVES_ICI | 0.926 ± 0.011 | 0.667 ± 0.044 | 0.652 ± 0.044 |\n", "| BirdNET_ICI | 0.884 ± 0.018 | 0.497 ± 0.028 | 0.580 ± 0.031 |\n", "| WhAM_ICI | 0.921 ± 0.005 | 0.722 ± 0.027 | 0.598 ± 0.028 |\n", "\n", "## Year holdouts\n", "\n", "| Model | Year | Unit | Type | ID |\n", "|---|---:|---:|---:|---:|\n", "| AVES_ICI | 2005 | 0.675 | 0.440 | 0.263 |\n", "| AVES_ICI | 2008 | 0.498 | 0.332 | 0.330 |\n", "| AVES_ICI | 2009 | 0.835 | 0.733 | 0.376 |\n", "| AVES_ICI | 2010 | 0.354 | 0.303 | 0.057 |\n", "| BirdNET_ICI | 2005 | 0.646 | 0.238 | 0.229 |\n", "| BirdNET_ICI | 2008 | 0.355 | 0.239 | 0.195 |\n", "| BirdNET_ICI | 2009 | 0.819 | 0.718 | 0.126 |\n", "| BirdNET_ICI | 2010 | 0.323 | 0.330 | 0.101 |\n", "| WhAM_ICI | 2005 | 0.773 | 0.406 | 0.305 |\n", "| WhAM_ICI | 2008 | 0.410 | 0.367 | 0.459 |\n", "| WhAM_ICI | 2009 | 0.856 | 0.961 | 0.255 |\n", "| WhAM_ICI | 2010 | 0.451 | 0.382 | 0.146 |\n", "\n", "All per-run support counts and secondary scores are saved in `feature_fusion_metrics.csv`. Year holdouts control local probe training, not unknown historical pretraining exposure." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "# Descriptive channel probes\n", "\n", "Post-hoc analysis of 12 fixed checkpoints. Encoders are not retrained. Every channel uses the same supervised, training-only frozen-probe protocol as fusion. Re-extracted fusion embeddings are checked against saved embeddings. These are diagnostic scores, not replacements selected for the primary result.\n", "\n", "## date_grouped: Dual_U1T1I0\n", "\n", "| Output | Unit | Type | ID |\n", "|---|---:|---:|---:|\n", "| rhythm | 0.558 ± 0.091 | 0.917 ± 0.065 | 0.325 ± 0.144 |\n", "| spectral | 0.516 ± 0.149 | 0.057 ± 0.023 | 0.098 ± 0.069 |\n", "| fusion | 0.598 ± 0.078 | 0.692 ± 0.088 | 0.248 ± 0.130 |\n", "\n", "## date_grouped: Dual_U1T1I1\n", "\n", "| Output | Unit | Type | ID |\n", "|---|---:|---:|---:|\n", "| rhythm | 0.552 ± 0.101 | 0.911 ± 0.071 | 0.320 ± 0.150 |\n", "| spectral | 0.511 ± 0.125 | 0.055 ± 0.032 | 0.074 ± 0.045 |\n", "| fusion | 0.596 ± 0.052 | 0.628 ± 0.074 | 0.223 ± 0.175 |\n", "\n", "## random: Dual_U1T1I0\n", "\n", "| Output | Unit | Type | ID |\n", "|---|---:|---:|---:|\n", "| rhythm | 0.639 ± 0.027 | 0.983 ± 0.023 | 0.459 ± 0.052 |\n", "| spectral | 0.863 ± 0.013 | 0.158 ± 0.023 | 0.367 ± 0.116 |\n", "| fusion | 0.901 ± 0.020 | 0.865 ± 0.115 | 0.597 ± 0.051 |\n", "\n", "## random: Dual_U1T1I1\n", "\n", "| Output | Unit | Type | ID |\n", "|---|---:|---:|---:|\n", "| rhythm | 0.632 ± 0.028 | 0.982 ± 0.019 | 0.461 ± 0.054 |\n", "| spectral | 0.861 ± 0.001 | 0.169 ± 0.021 | 0.443 ± 0.059 |\n", "| fusion | 0.907 ± 0.010 | 0.735 ± 0.091 | 0.581 ± 0.012 |\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for report in ['feature_fusion','branch_probes']:\n", " path = ROOT / 'reports_v3' / f'{report}.md'\n", " if path.exists():\n", " display(Markdown(path.read_text()))" ] }, { "cell_type": "markdown", "id": "8e15527a", "metadata": {}, "source": [ "## Reproduction\n", "\n", "Run `scripts_v3/experiments.py` for the 102 DCCE/control evaluations and `scripts_v3/aves_finetune.py` for the 12 pretrained-transformer adaptations. `scripts_v3/year_holdout.py` adds the 24 strict year-holdout evaluations. Runners verify existing source hashes before skipping completed outputs. Analysis scripts regenerate tables and confidence intervals. Consult `README_v3.md`; training is intentionally not an automatic notebook side effect." ] } ], "metadata": { "kernelspec": { "display_name": "Paper revision V3", "language": "python", "name": "review_v3" }, "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 }