{"id":"W2799430123","doi":"10.1007/s10844-018-0505-8","title":"Granular methods in automatic music genre classification: a case study","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Music and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scalability; Rough set; Artificial intelligence; Machine learning; Focus (optics); Set (abstract data type); Statistical classification; Granular computing; Algorithm; Fuzzy set; Fuzzy logic; Data mining; Database","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.003304262,0.0001250267,0.0002897593,0.0006005117,0.0001296201,0.0004971381,0.0005387859,0.00005694411,0.00001872899],"category_scores_gemma":[0.0001362137,0.00009535265,0.00007203393,0.000765466,0.00003139867,0.002846091,0.00007041493,0.0001901628,0.00007145717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405181,"about_ca_system_score_gemma":0.0001604536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004882081,"about_ca_topic_score_gemma":0.000006714929,"domain_scores_codex":[0.9973372,0.0002953891,0.001651667,0.0000914222,0.0004569996,0.0001673229],"domain_scores_gemma":[0.9974418,0.0001066138,0.001291182,0.0003177074,0.0007488788,0.00009382825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001775241,0.0003418935,0.003019434,0.0003562861,0.00007585351,0.000576689,0.1586033,0.0009871082,0.0002027974,0.003863092,0.002629032,0.8293267],"study_design_scores_gemma":[0.0009756452,0.0007909475,0.00168691,0.0005451705,0.00003382064,0.01913246,0.08866599,0.8658978,0.0009691357,0.0002265816,0.02071465,0.00036083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1490485,0.0001400011,0.8481658,0.00009756292,0.001305985,0.0002769602,2.870929e-7,0.00002959975,0.0009353494],"genre_scores_gemma":[0.9615909,0.000005175479,0.03793037,0.0002395203,0.0001963562,0.00000999215,3.46115e-7,0.000003963549,0.00002331902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8649107,"threshold_uncertainty_score":0.4793914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08951643055732517,"score_gpt":0.3717848257713616,"score_spread":0.2822683952140364,"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."}}