{"id":"W7082624330","doi":"10.5281/zenodo.17173901","title":"Music composition with AI","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Music and artificial intelligence; Pop music automation; Musical composition; Generative grammar; Composition (language); Focus (optics); Programming; Key (lock); Popular music","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":[0.002694292,0.0009098805,0.0004965483,0.001215149,0.002218518,0.006465248,0.001530941,0.002614276,0.02755656],"category_scores_gemma":[0.007545559,0.0004083287,0.0007414691,0.0006866457,0.008920068,0.008479211,0.005458741,0.00312033,0.007979293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312233,"about_ca_system_score_gemma":0.001038154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007950572,"about_ca_topic_score_gemma":0.0007651184,"domain_scores_codex":[0.9979797,0.0006708138,0.0001176129,0.0004655174,0.0006455104,0.0001207726],"domain_scores_gemma":[0.998049,0.0008588209,0.0001200671,0.0005554138,0.0002312767,0.0001854186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000979844,0.00005363678,0.0006944565,0.0004234637,0.00005718924,0.000175511,0.001348176,0.005155617,0.003163757,0.7806028,0.02337451,0.1848529],"study_design_scores_gemma":[0.00003459766,0.00006330351,0.0003716709,0.0002567538,0.00002282197,0.0004031884,0.0004288107,0.008782818,0.002298343,0.568917,0.4183713,0.00004932951],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01046907,0.01549203,0.481472,0.02808171,0.00518273,0.0002263237,0.0002683253,0.002605844,0.456202],"genre_scores_gemma":[0.3804692,0.01384702,0.4068031,0.009359482,0.004616442,0.0003792007,0.0006370537,0.001601567,0.1822869],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02755656,"threshold_uncertainty_score":0.09218591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313931164990214,"score_gpt":0.2901199439023567,"score_spread":0.2587268274033354,"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."}}