{"id":"W2042645344","doi":"10.1162/comj.2008.32.1.60","title":"Feature Set Patterns in Music","year":2008,"lang":"en","type":"article","venue":"Computer Music Journal","topic":"Music and Audio Processing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"City, University of London; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Feature (linguistics); Set (abstract data type); Computer science; Pattern recognition (psychology); Speech recognition; Artificial intelligence; Linguistics; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003255611,0.0002186429,0.0002726922,0.0002783594,0.0003488707,0.0003305885,0.001171157,0.00009599508,0.00007747758],"category_scores_gemma":[0.000008831504,0.0001875392,0.000118292,0.0005027974,0.00004982889,0.0008728788,0.0003792113,0.0007629713,0.00004909867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007756562,"about_ca_system_score_gemma":0.00019875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000575319,"about_ca_topic_score_gemma":0.00001151551,"domain_scores_codex":[0.9983013,0.0001008204,0.0002911137,0.0003841046,0.0004082978,0.000514355],"domain_scores_gemma":[0.9990823,0.00004866677,0.0001848708,0.0003899242,0.00009414999,0.0002001259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001101022,0.0002023197,0.0311483,0.00005908986,0.00004592343,0.005619695,0.01631573,0.001223211,0.000233624,0.001283247,0.5017991,0.4420588],"study_design_scores_gemma":[0.006230691,0.0005101595,0.5015277,0.001407655,0.00002383938,0.04766484,0.0001586307,0.266616,0.0005682623,0.009098899,0.1638677,0.002325663],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2754645,0.0002388565,0.7190976,0.00284511,0.001812123,0.0000602349,9.144201e-7,0.00007564007,0.0004050389],"genre_scores_gemma":[0.9157174,0.00005280673,0.07205394,0.009609687,0.002238919,0.000003000519,0.000002253299,0.00001975006,0.0003022638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6470436,"threshold_uncertainty_score":0.7647628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04513228415850841,"score_gpt":0.2384528665808129,"score_spread":0.1933205824223045,"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."}}