{"id":"W4318475782","doi":"10.1101/2023.01.29.526133","title":"Spectro-temporal acoustical markers differentiate speech from song across cultures","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; Université Laval; International Laboratory for Brain, Music and Sound Research; McGill University; Centre for Interdisciplinary Research in Music Media and Technology; Centre for Research on Brain Language and Music","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Singing; Sentence; Feature (linguistics); Modulation (music); Speech recognition; Range (aeronautics); Communication; Psychology; Acoustics; Computer science; Linguistics; Artificial intelligence","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":[0.0004755358,0.0001454827,0.0002591795,0.0004956592,0.0001973244,0.0005812997,0.0001277494,0.0003156318,0.00354247],"category_scores_gemma":[0.002442827,0.0001130406,0.000157763,0.0003571223,0.0004236515,0.0003207826,0.0003907465,0.000230592,0.00057838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000876077,"about_ca_system_score_gemma":0.00007860494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005358707,"about_ca_topic_score_gemma":0.0010855,"domain_scores_codex":[0.9997913,0.00005707286,0.00002198213,0.00005706098,0.00004781635,0.00002488181],"domain_scores_gemma":[0.9985169,0.0005947524,0.0003909565,0.0001625917,0.00021858,0.0001163696],"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":[0.001340382,0.0001142641,0.4002606,0.0003265348,0.0002267988,0.0002588881,0.001975638,0.001009409,0.4892955,0.0008091704,0.0009930066,0.1033898],"study_design_scores_gemma":[0.000006823172,0.0001081425,0.9815709,0.00001988265,0.00003100696,0.0003324689,0.0007090884,0.001271777,0.0144215,0.0007861186,0.0007240945,0.00001825514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957482,0.0002198787,0.001958902,0.0001302108,0.00001873684,0.000004929782,0.000259218,0.00002000736,0.001639947],"genre_scores_gemma":[0.9984319,0.00006335061,0.0009404576,0.00003478339,0.00001527018,0.000004379955,0.0001228211,0.000009965647,0.0003771477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00354247,"threshold_uncertainty_score":0.01185077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0250225021662766,"score_gpt":0.2833388496803342,"score_spread":0.2583163475140576,"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."}}