{"id":"W1571854457","doi":"","title":"Music in Motion . The Automated Transcription for Indian Music(AUTRIM).","year":2014,"lang":"en","type":"article","venue":"MUSICultures","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Transcription (linguistics); Motion (physics); Computer science; Artificial intelligence; Linguistics; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048148,0.0001589255,0.0001576745,0.00009247795,0.0002868289,0.0002901339,0.000578919,0.0001017197,0.00001323529],"category_scores_gemma":[0.00006195396,0.0001006822,0.00008283648,0.0004746433,0.00004851028,0.0004543605,0.00004530187,0.0001403789,0.00001389891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003589422,"about_ca_system_score_gemma":0.00002707644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006043336,"about_ca_topic_score_gemma":0.000314765,"domain_scores_codex":[0.9987939,0.00009400525,0.0002359975,0.0003597065,0.0002032306,0.0003131859],"domain_scores_gemma":[0.9993999,0.00006691438,0.0001029747,0.0003114259,0.00006882397,0.00004997715],"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":[0.00003005882,0.0002923324,0.000362402,0.0002795495,0.0000350302,0.000008980397,0.05173086,0.002256004,0.06003212,0.07240838,0.1330919,0.6794723],"study_design_scores_gemma":[0.004971535,0.0003489029,0.1466978,0.0004314769,0.00006090054,0.00006776754,0.0009988321,0.5196272,0.03841637,0.03721727,0.2496222,0.001539749],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5196574,0.0002362703,0.4639033,0.009113723,0.001933103,0.001046634,0.000006720245,0.001273675,0.002829204],"genre_scores_gemma":[0.9906231,0.000002015415,0.004343869,0.004497875,0.0003245904,0.00007987671,0.000013691,0.000009876239,0.00010506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6779326,"threshold_uncertainty_score":0.4105703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243246310594664,"score_gpt":0.2373750405969372,"score_spread":0.2149425774909906,"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."}}