{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01187266,0.000866816,0.0007905941,0.004931156,0.0009241126,0.002062417,0.001434562,0.001258094,0.001850699],"category_scores_gemma":[0.0491331,0.0004578324,0.001177783,0.002066616,0.00117795,0.002099304,0.002920291,0.001744798,0.0008304628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006234035,"about_ca_system_score_gemma":0.0005540687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002279709,"about_ca_topic_score_gemma":0.003414514,"domain_scores_codex":[0.9905105,0.003430796,0.0007359658,0.002441958,0.002436307,0.0004444668],"domain_scores_gemma":[0.9617918,0.02587046,0.0035483,0.00521021,0.003009855,0.0005693627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00173056,0.0004713992,0.5586835,0.0007662186,0.001574752,0.0002858735,0.002571357,0.07281239,0.08868267,0.004700493,0.005841769,0.261879],"study_design_scores_gemma":[0.0001025735,0.0004588397,0.381472,0.00009040967,0.0002370797,0.0003719275,0.0007360639,0.5563948,0.04554576,0.007755084,0.006564873,0.0002705982],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7171796,0.0002397427,0.2689322,0.0001812023,0.0001009784,0.0003976122,0.004566357,0.004728004,0.003674369],"genre_scores_gemma":[0.84764,0.00003988593,0.1456324,0.0001045032,0.00003278287,0.0003136546,0.005311431,0.0004144568,0.0005109366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01187266,"threshold_uncertainty_score":0.06278938,"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."}}