{"id":"W4410110965","doi":"10.7202/1117692ar","title":"Understanding Microtuning Through Recording Analysis: The Transcription and Analysis of Microtuning in Five Recordings from a Variety of Musical Traditions","year":2021,"lang":"en","type":"article","venue":"Intersections Canadian Journal of Music","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Musical; Transcription (linguistics); Musical analysis; Cognitive science; Communication; Psychology; Art; Computer science; Linguistics; Literature; Philosophy; Artificial intelligence","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":[0.0004651815,0.0001208816,0.0004906161,0.001402119,0.0002837521,0.0001410378,0.0003133598,0.000070415,0.0001052353],"category_scores_gemma":[0.00008208408,0.0001105207,0.0003977831,0.004772861,0.0001664983,0.0006108037,0.00002004327,0.0003708678,1.110779e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002512148,"about_ca_system_score_gemma":0.0004613062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02390836,"about_ca_topic_score_gemma":0.2159196,"domain_scores_codex":[0.9985399,0.0001750492,0.0006876954,0.0002363207,0.0001527533,0.0002083143],"domain_scores_gemma":[0.9987008,0.0002617666,0.0004794056,0.000220553,0.0002102156,0.0001272661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009935151,0.0002944268,0.08250228,0.0001470002,0.02741941,0.0005572399,0.6403222,0.03551433,0.1620148,0.02353786,0.002376686,0.02521445],"study_design_scores_gemma":[0.004028027,0.0006168404,0.258678,0.004254479,0.02959589,0.0009837666,0.2976313,0.3443594,0.01611515,0.04001962,0.001972743,0.001744801],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5152925,0.0002062718,0.4830421,0.0009993968,0.0002486388,0.00003004402,0.00002249902,0.000003136762,0.0001554559],"genre_scores_gemma":[0.9891315,0.00003292095,0.01050144,0.0002654908,0.00004542832,0.000001621878,0.000006754644,0.000006149241,0.000008758751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.473839,"threshold_uncertainty_score":0.9825915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07874509742710986,"score_gpt":0.2442844803254396,"score_spread":0.1655393828983298,"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."}}