{"id":"W2992857896","doi":"","title":"Perception of meter similarity in flamenco music","year":2007,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"Centre for Interdisciplinary Research in Music Media and Technology","keywords":"Similarity (geometry); Multidimensional scaling; Metric (unit); Perception; Headphones; Active listening; Matrix (chemical analysis); Point (geometry); Mathematics; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Computer science; Statistics; Psychology; Communication; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003557523,0.00006630324,0.0000961364,0.0002259026,0.00004545278,0.00003092888,0.0002915785,0.00006825727,0.00005752419],"category_scores_gemma":[0.00005226305,0.00006993073,0.00002127935,0.0003445036,0.00003992071,0.0001461688,0.00002994939,0.0001123199,0.00000794107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001749099,"about_ca_system_score_gemma":0.0003013779,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007936848,"about_ca_topic_score_gemma":0.02618488,"domain_scores_codex":[0.9992611,0.0000112913,0.0001732344,0.0001536896,0.0001162928,0.0002843327],"domain_scores_gemma":[0.9994849,0.00003295613,0.00004397818,0.0002038709,0.00005705134,0.0001772601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000007676436,0.00008893376,0.02160622,0.0002266271,0.00001876721,0.0004455663,0.01082026,0.002006408,0.1823113,0.006814274,0.02360396,0.75205],"study_design_scores_gemma":[0.0007687471,0.0001098171,0.8341386,0.0001955518,0.00002811999,0.00003911813,0.001455279,0.1464238,0.001952347,0.008024368,0.006070185,0.0007940109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.394016,0.00002927926,0.6004608,0.0002433295,0.0002451854,0.0000522075,0.000003150968,0.00001554532,0.00493452],"genre_scores_gemma":[0.9661976,0.000002773291,0.03197935,0.001659903,0.00005693038,5.754089e-7,0.0000013465,0.000004264444,0.00009722098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8125324,"threshold_uncertainty_score":0.9986694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065627927276122,"score_gpt":0.2383297638038367,"score_spread":0.2176734845310754,"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."}}