{"id":"W4411759485","doi":"10.1101/2025.06.26.661638","title":"Automated identification of individual birds by song enables multi-year recapture from passive acoustic monitoring data","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Fish and Wildlife Foundation","keywords":"Mark and recapture; Identification (biology); Computer science; Geography; Speech recognition; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003815721,0.0003906097,0.0003872184,0.0001135643,0.0001290221,0.0001445371,0.002034978,0.0008165939,0.0000116925],"category_scores_gemma":[0.0003987062,0.0004306921,0.0000953836,0.0002378078,0.0001312762,0.00001926905,0.002381423,0.0005040534,0.000008985075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005869572,"about_ca_system_score_gemma":0.0004361244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001415447,"about_ca_topic_score_gemma":0.0000070229,"domain_scores_codex":[0.9975792,0.0002001508,0.0006431135,0.0009779048,0.0003129781,0.0002867061],"domain_scores_gemma":[0.9959664,0.00003746866,0.0006226485,0.002756581,0.0004935051,0.0001234441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003585778,0.0001948248,0.008741152,0.0001159811,0.0002051015,0.000001837661,0.000007605766,0.00003850407,0.9885387,0.000004499863,0.002106619,0.000009299844],"study_design_scores_gemma":[0.0004352599,0.00003890775,0.09598902,0.0002736734,0.0003145356,4.045318e-9,0.00003634469,0.001019682,0.8996673,2.206599e-7,0.001770686,0.0004543557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979301,0.003943555,0.00501275,0.00006753441,0.0006685625,0.0005490463,0.01018217,0.0002730621,0.000002315934],"genre_scores_gemma":[0.9921448,0.0008506236,0.006438916,0.00003995782,0.0002069266,0.00008881839,0.0001446302,0.0000603562,0.00002498164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0888714,"threshold_uncertainty_score":0.9998145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03246952651655203,"score_gpt":0.2874129987059789,"score_spread":0.2549434721894269,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}