{"id":"W2891885850","doi":"10.1101/411074","title":"Discerning the functional networks behind processing of music and speech through human vocalizations","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Laboratory for Brain, Music and Sound Research","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Universidad Nacional Autónoma de México; Consejo Nacional de Ciencia y Tecnología","keywords":"Planum temporale; Psychology; Melody; Musicality; Functional magnetic resonance imaging; Active listening; Lyrics; Superior temporal gyrus; Speech perception; Cognitive psychology; Perception; Premotor cortex; Auditory cortex; Communication; Neuroscience; Musical; Biology","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":[0.0001388133,0.0001233448,0.00009708688,0.0002933793,0.0001200697,0.0003411155,0.0000900097,0.0002058615,0.002475678],"category_scores_gemma":[0.0004727408,0.0001310888,0.0001284316,0.0001303951,0.0002516571,0.0002187977,0.0001962498,0.0001137058,0.0001927098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037905,"about_ca_system_score_gemma":0.0001114669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001143047,"about_ca_topic_score_gemma":0.001683314,"domain_scores_codex":[0.9999392,0.0000150797,0.000002070338,0.00002332528,0.000006919004,0.00001338875],"domain_scores_gemma":[0.9998642,0.00007144746,0.00002777293,0.000007267242,0.00001105261,0.0000181899],"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":[0.0007514623,0.00008803394,0.04726845,0.0001623271,0.0001278058,0.0004386475,0.001609675,0.001594668,0.8980436,0.001754122,0.0006343185,0.04752697],"study_design_scores_gemma":[0.00003173379,0.0001544155,0.9678729,0.00002214837,0.00005085245,0.0004151064,0.0005156551,0.01121476,0.01600543,0.002817012,0.0008837978,0.00001608868],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906647,0.0002404042,0.00637812,0.00008948918,0.000008142214,0.00002457648,0.0002137777,0.00004112647,0.002339667],"genre_scores_gemma":[0.9978022,0.00005731791,0.001381485,0.00001800649,0.000006113624,0.0000232777,0.00007796351,0.000007993549,0.0006256117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002475678,"threshold_uncertainty_score":0.008281946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0518277014801819,"score_gpt":0.2650668659196494,"score_spread":0.2132391644394675,"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."}}