{"id":"W2603522029","doi":"10.1002/ece3.2730","title":"Designing better frog call recognition models","year":2017,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint John Regional Hospital; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada","keywords":"Computer science; Bioacoustics; Variable (mathematics); Training (meteorology); Lithobates; Scope (computer science); Artificial intelligence; Speech recognition; Machine learning; Ecology; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001214485,0.00005685162,0.00005689923,0.00001444077,0.0004101442,0.00001995247,0.0001065278,0.0001527852,0.00001604107],"category_scores_gemma":[0.00004408917,0.0000567585,0.00002341056,0.000006351199,0.000112001,0.00001123487,0.00009791053,0.00007133908,0.00002614025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009876664,"about_ca_system_score_gemma":0.00001520602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002601026,"about_ca_topic_score_gemma":0.0001413204,"domain_scores_codex":[0.9996051,0.00004741885,0.00007658153,0.0001421467,0.00002461837,0.0001041804],"domain_scores_gemma":[0.9996394,0.000005735719,0.00006077623,0.0002202325,0.00004027002,0.00003359391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002171616,0.0001300015,0.2240163,0.0000114821,0.00004008375,0.000003357448,0.00007142056,0.000016116,0.7541377,0.0006208374,0.00482474,0.01591077],"study_design_scores_gemma":[0.0005279704,0.0002799289,0.9790167,0.000007654456,0.00003141235,0.00001727655,0.00003578895,0.0006335356,0.0131785,0.004350254,0.001752899,0.0001680529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888533,0.0001551495,0.008127167,0.0004903982,0.00007574291,0.00007067229,0.000004247457,0.000009048783,0.002214311],"genre_scores_gemma":[0.9970028,0.00007947241,0.002134058,0.0002590564,0.00007988796,0.00001737939,0.00005863613,0.000005181205,0.0003634915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7550004,"threshold_uncertainty_score":0.3154539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04678805896344063,"score_gpt":0.2830450640391602,"score_spread":0.2362570050757196,"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."}}