{"id":"W3160948666","doi":"10.1111/2041-210x.13644","title":"Using identity calls to detect structure in acoustic datasets","year":2021,"lang":"en","type":"article","venue":"Methods in 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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Carlsbergfondet; Nova Scotia Research Innovation Trust; Killam Trusts; Faculty of Graduate Studies, Dalhousie University; Explorers Club; Natur og Univers, Det Frie Forskningsråd; Nando and Elsa Peretti Foundation; Danmarks Frie Forskningsfond; Dalhousie University; Villum Fonden; National Geographic Society; PADI Foundation; Oticon Fonden; Mitacs","keywords":"Taxon; Identity (music); Biology; Range (aeronautics); Subspecies; Identification (biology); Evolutionary biology; Ecology","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":[0.0003972708,0.00006220955,0.0001007521,0.00005082177,0.00004885865,0.00000756213,0.00008591735,0.0001709859,0.00002225052],"category_scores_gemma":[0.000423456,0.00006694172,0.00001463158,0.0001475693,0.00004108821,0.000006880175,0.0002027172,0.0001238279,0.000001155037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004460169,"about_ca_system_score_gemma":0.00005164526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005604936,"about_ca_topic_score_gemma":0.008222017,"domain_scores_codex":[0.9990192,0.0004845082,0.0001370253,0.0002007079,0.00002819086,0.0001303447],"domain_scores_gemma":[0.9996959,0.00002919324,0.00002547582,0.0001886156,0.00002619267,0.00003464806],"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":[0.00002856956,0.00002146614,0.0679052,0.000006246006,0.000002950893,0.000005286338,0.00001495736,0.000283931,0.9292576,0.00006236823,0.00002764063,0.002383803],"study_design_scores_gemma":[0.0002804521,0.0000750015,0.946201,0.000007962071,0.00001398506,0.00003822563,0.00008228065,0.0006596096,0.05064512,0.001240215,0.0006469992,0.0001091113],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8932019,0.000454261,0.1060908,0.0000492622,0.00008620381,0.00006985672,0.00002437545,0.000002498341,0.00002086481],"genre_scores_gemma":[0.8422971,0.00004287136,0.1573921,0.0001621643,0.000017003,0.000004718075,0.00006455396,0.000003705204,0.00001576063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8786125,"threshold_uncertainty_score":0.4588078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05397831359052291,"score_gpt":0.4342330556939321,"score_spread":0.3802547421034092,"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."}}