{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004086031,0.000138254,0.0001218656,0.0001331993,0.001766473,0.002426343,0.001037444,0.00005967205,0.001085431],"category_scores_gemma":[0.0001271659,0.0001400588,0.00003353111,0.0005509707,0.0001463883,0.0005104726,0.001048903,0.0002152749,0.0002735144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007445826,"about_ca_system_score_gemma":0.000009002633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001816904,"about_ca_topic_score_gemma":0.000001790395,"domain_scores_codex":[0.9984637,0.0001147218,0.0001686806,0.0005562776,0.000344452,0.0003520993],"domain_scores_gemma":[0.9989159,0.00001843697,0.0001150089,0.0004716863,0.0003297855,0.0001492284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008211302,0.00007430978,0.00001240414,0.00006125976,0.00001630504,0.0000154308,0.002435555,0.000006812354,0.01630828,0.01379326,0.6839852,0.283283],"study_design_scores_gemma":[0.0002700382,0.00006510213,0.001176408,0.00002414423,0.000006440314,0.00008200764,0.000308728,0.009338679,0.0006955807,0.0002131192,0.9876258,0.0001939509],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06332816,0.0003170253,0.3366413,0.3531201,0.000402172,0.0009888033,0.0001492636,0.002342023,0.2427111],"genre_scores_gemma":[0.9864661,0.00003526158,0.004126602,0.003453135,0.0003076157,6.992215e-8,0.0001799766,0.000422003,0.005009282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9231379,"threshold_uncertainty_score":0.9998277,"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."}}