{"id":"W3015851848","doi":"10.1007/978-3-030-43859-3_11","title":"Objective Evaluation of Tonal Fitness for Chord Progressions Using the Tonal Interval Space","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Music and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Chord (peer-to-peer); Computer science; Speech recognition; Active listening; Perception; Artificial intelligence; Communication; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001715315,0.0003531443,0.0004266,0.0002613724,0.0003913924,0.0003305744,0.002264372,0.0001732145,0.00001618661],"category_scores_gemma":[0.0002627156,0.0002513459,0.0001665568,0.000554827,0.0007453734,0.0004857007,0.001069387,0.000534426,0.000001654879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000267067,"about_ca_system_score_gemma":0.001795881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007784399,"about_ca_topic_score_gemma":0.00001671902,"domain_scores_codex":[0.9965324,0.00007141649,0.000448487,0.001050522,0.001508207,0.0003889776],"domain_scores_gemma":[0.9973966,0.0005035924,0.0005070175,0.0006195355,0.000877534,0.0000957602],"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":[0.00001892851,0.00003073453,0.00003767432,0.00009153596,0.00002667949,0.000004868411,0.002889744,0.0519689,0.000677608,0.01547012,0.00004037904,0.9287428],"study_design_scores_gemma":[0.0002846795,0.0001211066,0.0000802055,0.0006959521,0.0000338168,0.0000252224,0.000001310547,0.8954022,0.005057993,0.09772307,0.0002816788,0.0002927538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004317831,0.0004477434,0.9938071,0.002480233,0.001262401,0.0008283222,0.000009509982,0.00004403921,0.0006888895],"genre_scores_gemma":[0.5142752,0.000003885679,0.4835142,0.001238322,0.0008497703,0.0000365421,0.000005528946,0.00002851676,0.00004805192],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.92845,"threshold_uncertainty_score":0.9999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0686454552781229,"score_gpt":0.3339775187242687,"score_spread":0.2653320634461458,"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."}}