{"id":"W4294845348","doi":"10.1101/2022.09.05.506691","title":"White matter structural bases for predictive tapping synchronization","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":4,"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; Secretaría de Ciencia, Tecnología e Innovación del Distrito Federal; Universidad Nacional Autónoma de México; Consejo Nacional de Ciencia y Tecnología","keywords":"Entrainment (biomusicology); Metronome; Rhythm; Tapping; White matter; Corpus callosum; Anatomy; Audiology; Neuroscience; Psychology; Physics; Biology; Medicine; Acoustics; Magnetic resonance imaging","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":[0.0001525285,0.0002015815,0.0001140834,0.0005615863,0.0001000586,0.0001957613,0.00006879213,0.0001291778,0.001315348],"category_scores_gemma":[0.001402622,0.0001118856,0.00007150774,0.0002078847,0.0002158908,0.0001553489,0.0001894504,0.0001120195,0.00009117687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005939528,"about_ca_system_score_gemma":0.00006396173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009906467,"about_ca_topic_score_gemma":0.001384897,"domain_scores_codex":[0.9999549,0.000006280194,0.000004485903,0.00001857739,0.00000981315,0.000005923312],"domain_scores_gemma":[0.9994036,0.0001783686,0.0002395109,0.00005878675,0.00005720501,0.00006261769],"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":[0.0007871657,0.00005391979,0.2244918,0.00008187249,0.0001039113,0.0006811619,0.0004926673,0.001096954,0.7469777,0.0002833619,0.0001191438,0.02483023],"study_design_scores_gemma":[0.000004730889,0.00007623787,0.9946273,0.000003251527,0.00001033976,0.0003266969,0.000038725,0.0008383603,0.003890394,0.0001225352,0.00005811576,0.000003283933],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985966,0.0000479363,0.001047883,0.000006603653,0.000001065427,0.00000476237,0.00006284197,0.00001339222,0.00021881],"genre_scores_gemma":[0.9996177,0.00001544169,0.0002407605,0.000002688927,0.000002917002,0.00000272103,0.00004919518,0.00000225223,0.00006632307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001315348,"threshold_uncertainty_score":0.004400313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02241227691108046,"score_gpt":0.2468032740910757,"score_spread":0.2243909971799952,"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."}}