{"id":"W4392983932","doi":"10.32920/25413049","title":"Audiovisual Interval Size Estimation Is Associated with Early Musical Training","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Multisensory perception and integration","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"GRAMMY Foundation","keywords":"Musical; Estimation; Interval (graph theory); Computer science; Training (meteorology); Speech recognition; Statistics; Mathematics; Art; Visual arts; Geography; Economics; Combinatorics","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":[0.0003173411,0.0002438759,0.0001938302,0.0004812227,0.00009571975,0.0002877959,0.000169612,0.0002587839,0.002329333],"category_scores_gemma":[0.002642093,0.0001178065,0.0001068885,0.0002192559,0.0002052232,0.0001711761,0.0002737068,0.0002286206,0.0002252116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008610872,"about_ca_system_score_gemma":0.00005235951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007243052,"about_ca_topic_score_gemma":0.0008330005,"domain_scores_codex":[0.9998131,0.00003187075,0.00002393449,0.00005997359,0.00004827495,0.00002284559],"domain_scores_gemma":[0.9964954,0.001094744,0.001633183,0.000169839,0.0002442306,0.0003626004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001374107,0.0002266959,0.8879608,0.00005965797,0.0000628811,0.0003662462,0.0005866355,0.0002239055,0.08915443,0.00003805894,0.00009463954,0.01985192],"study_design_scores_gemma":[0.000002079411,0.0001595568,0.9980754,0.000002685282,0.000005902917,0.0002084771,0.00006346954,0.0002078767,0.001217602,0.00001080851,0.00004375518,0.000002417511],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993236,0.00009979444,0.0002977093,0.000007848129,0.000002438063,0.000002746626,0.00002338199,0.000006919712,0.000235585],"genre_scores_gemma":[0.9996206,0.0000328293,0.0001338446,0.000004057251,0.000004222835,0.000002158511,0.0000393858,0.000002149735,0.0001607367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002329333,"threshold_uncertainty_score":0.007792354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065500367090923,"score_gpt":0.3908281880486051,"score_spread":0.2842781513395128,"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."}}