{"id":"W4399381616","doi":"10.1038/s41467-024-49040-3","title":"Spectro-temporal acoustical markers differentiate speech from song across cultures","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; Université Laval; McGill University; International Laboratory for Brain, Music and Sound Research; Centre for Interdisciplinary Research in Music Media and Technology","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Health and Human Services; Government of Canada; National Institutes of Health; Canadian Institutes of Health Research; Royal Society Te Apārangi; National Center for Advancing Translational Sciences; Fonds de Recherche du Québec - Santé; Fondation Pour l'Audition","keywords":"Temporal scales; Modulation (music); Range (aeronautics); Feature (linguistics); Speech recognition; Communication; Computer science; Psychology; Biology; Acoustics; Linguistics; Ecology; Physics","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":[0.0002650372,0.000148701,0.0001893332,0.000501542,0.0001995249,0.0005321722,0.00009954816,0.0002452191,0.001798915],"category_scores_gemma":[0.002056916,0.0001480515,0.0001597731,0.0003686729,0.000487914,0.0003095451,0.0004284293,0.0002167609,0.0003833839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008044284,"about_ca_system_score_gemma":0.00006270732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007320498,"about_ca_topic_score_gemma":0.001598135,"domain_scores_codex":[0.9998247,0.00005229638,0.00001741838,0.00004768002,0.00003220203,0.00002578793],"domain_scores_gemma":[0.9990085,0.0004069781,0.0002881734,0.0001130726,0.0001056851,0.00007761134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008738956,0.00008709881,0.4770547,0.0001783612,0.0002408146,0.0003044278,0.00332042,0.0009087115,0.4320325,0.0006232676,0.0002801909,0.08409562],"study_design_scores_gemma":[0.000003715011,0.00007671977,0.9896742,0.000009851704,0.00002481238,0.0002810548,0.000975341,0.0009592199,0.007325984,0.0003468483,0.000311126,0.00001115423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997734,0.00009657795,0.0009706096,0.00002835222,0.000003850513,0.000002611704,0.00006217721,0.000006690445,0.00109522],"genre_scores_gemma":[0.9992437,0.00004530502,0.0005044796,0.00001150001,0.00000351677,0.000002604937,0.0000431105,0.000004329696,0.0001413682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001798915,"threshold_uncertainty_score":0.006018043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134637982216978,"score_gpt":0.3610262714072382,"score_spread":0.3396798915850684,"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."}}