{"id":"W2898932590","doi":"10.1080/09524622.2018.1538902","title":"Crowd intelligence can discern between repertoires of killer whale ecotypes","year":2018,"lang":"en","type":"article","venue":"Bioacoustics","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Office of Naval Research; National Geographic Society","keywords":"Ecotype; Phylogenetic tree; Context (archaeology); Repertoire; Biology; Population; Similarity (geometry); Evolutionary biology; Categorization; Whale; Ecology; Artificial intelligence; Computer science; Genetics; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00111362,0.0002829789,0.0002648747,0.001284283,0.0002868084,0.0006731748,0.0001865859,0.0003391803,0.001727822],"category_scores_gemma":[0.005203354,0.0002162119,0.000252049,0.0002484501,0.0007287963,0.0007003506,0.001341635,0.0003127905,0.0003254954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001194287,"about_ca_system_score_gemma":0.0001058406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001225078,"about_ca_topic_score_gemma":0.002490789,"domain_scores_codex":[0.9994673,0.0001528217,0.00004049288,0.000145966,0.0001200239,0.00007339862],"domain_scores_gemma":[0.9961653,0.001851106,0.0008062926,0.0004111566,0.0004196664,0.0003465281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006937904,0.00004988588,0.8957043,0.0001083974,0.000107671,0.0001249195,0.005078586,0.0005609497,0.06993493,0.0001535597,0.000169827,0.02731327],"study_design_scores_gemma":[0.000002636678,0.0001149906,0.9957228,0.00001125445,0.00002179867,0.0000822584,0.001591608,0.0004940171,0.001613231,0.0001814699,0.0001521952,0.0000116796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978149,0.00008130904,0.0008446937,0.00001033484,0.000005140482,0.000006478606,0.00004568285,0.000009895639,0.001181675],"genre_scores_gemma":[0.9991492,0.00003427275,0.0004938462,0.00000924042,0.000004437553,0.000008088125,0.00008454378,0.000005561014,0.0002107631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001727822,"threshold_uncertainty_score":0.005889475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601052952202718,"score_gpt":0.2583036216984728,"score_spread":0.2322930921764456,"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."}}