{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001989572,0.0008895768,0.0008545626,0.000886124,0.0004404576,0.001278481,0.001846066,0.001489708,0.005576518],"category_scores_gemma":[0.006522786,0.0008276228,0.001328178,0.0003840134,0.0004998887,0.001513366,0.000908774,0.001517418,0.001457442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100254,"about_ca_system_score_gemma":0.0009860599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008256251,"about_ca_topic_score_gemma":0.006827695,"domain_scores_codex":[0.9992608,0.0001904499,0.00005915795,0.0002350256,0.0001633908,0.00009102764],"domain_scores_gemma":[0.9978371,0.001437009,0.0001645203,0.0001551328,0.0003548381,0.00005145642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006811874,0.00005536494,0.001887828,0.00004641885,0.00004273247,0.00005607224,0.00004167472,0.961446,0.004093413,0.001926793,0.0005102503,0.0298252],"study_design_scores_gemma":[0.000003107957,0.000009249631,0.00008432948,0.000003186995,0.000004412569,0.000007492687,0.000004630078,0.9988213,0.0005993241,0.0003002463,0.0001596337,0.000003125663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.101815,0.0002304316,0.8898972,0.0003326001,0.00006495481,0.0002818757,0.0003293518,0.003071829,0.0039767],"genre_scores_gemma":[0.706798,0.0001939595,0.2858962,0.0002996039,0.00005014107,0.0007003234,0.0006956164,0.0004024141,0.004963832],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008256251,"threshold_uncertainty_score":0.0186553,"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."}}