{"id":"W188306515","doi":"10.5281/zenodo.1415575","title":"Combining Features Extracted From Audio, Symbolic And Cultural Sources.","year":2008,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Suite; Computer science; Feature (linguistics); Class (philosophy); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Seriousness; Data mining; Natural language processing; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.00004555653,0.0001113356,0.000137595,0.00003379378,0.0003058472,0.0001909678,0.0003037182,0.00005274654,0.00003948478],"category_scores_gemma":[0.00001884578,0.00008199939,0.00002676081,0.0001522532,0.00007924833,0.0005334941,0.0001526994,0.0001318237,0.00001725339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000756342,"about_ca_system_score_gemma":0.00002841476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001824369,"about_ca_topic_score_gemma":0.000007388103,"domain_scores_codex":[0.9992282,0.00002003576,0.0001155178,0.0002832098,0.0001595033,0.0001935733],"domain_scores_gemma":[0.9995843,0.0000528466,0.00005370668,0.0001837503,0.00003725386,0.00008816372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003208102,0.0003311071,0.09475572,0.00007451408,0.0002271037,0.0005341888,0.1020913,0.00009473413,0.09224358,0.05267124,0.2086518,0.4482926],"study_design_scores_gemma":[0.001956845,0.00008370765,0.9086723,0.0001517995,0.00002360119,0.0007017597,0.0007051901,0.01629609,0.03702827,0.005884915,0.02732425,0.00117121],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9387864,0.001099456,0.04587694,0.001178527,0.0001486035,0.00004327485,7.351971e-7,0.0002841151,0.01258195],"genre_scores_gemma":[0.9733042,0.00004134937,0.02291126,0.001696354,0.0000942073,0.000001961424,0.000002841548,0.000004807737,0.001942998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8139166,"threshold_uncertainty_score":0.3343838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074023640362547,"score_gpt":0.2313664163411105,"score_spread":0.2106261799374851,"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."}}