{"id":"W56995320","doi":"10.5281/zenodo.1417417","title":"Musical Genre Classification: Is It Worth Pursuing And How Can It Be Improved?","year":2006,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Subjectivity; Similarity (geometry); Musical; Genre analysis; Discipline; Data science; Music information retrieval; Musicology; Linguistics; Artificial intelligence; Natural language processing; Epistemology; Sociology; Social science; Pedagogy; Literature","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":[0.004961367,0.001318224,0.002967946,0.004204228,0.001852514,0.006476068,0.003490251,0.002188093,0.02012341],"category_scores_gemma":[0.02417556,0.0004172398,0.001353609,0.004051739,0.001191812,0.008778988,0.002008749,0.00253384,0.02664444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007855211,"about_ca_system_score_gemma":0.001487391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007509409,"about_ca_topic_score_gemma":0.01034625,"domain_scores_codex":[0.9976693,0.0006384713,0.0002409286,0.0005285477,0.0007059703,0.0002168001],"domain_scores_gemma":[0.9859188,0.002324894,0.0005426205,0.003441695,0.006524885,0.001247207],"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":[0.0004551665,0.0002090561,0.008827232,0.0004933377,0.0001108788,0.00005409882,0.0001589516,0.001231913,0.009147104,0.006039081,0.08696456,0.8863087],"study_design_scores_gemma":[0.000713423,0.00111912,0.08596422,0.002071073,0.0009972586,0.002344247,0.006016155,0.2952836,0.03039301,0.3025377,0.2718172,0.000742965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07682005,0.0204147,0.7332065,0.07929359,0.01282696,0.0008330936,0.006041896,0.02249964,0.04806363],"genre_scores_gemma":[0.2336954,0.01069153,0.6938047,0.006810068,0.007517221,0.0003720809,0.01306244,0.00243414,0.03161247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02012341,"threshold_uncertainty_score":0.06731957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05322688878533093,"score_gpt":0.2470623955776686,"score_spread":0.1938355067923377,"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."}}