{"id":"W2753868141","doi":"10.48550/arxiv.1903.07227","title":"Counterpoint By Convolution.","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gibbs sampling; Computer science; Sampling (signal processing); Convolution (computer science); Convolutional neural network; Artificial intelligence; Counterpoint; Set (abstract data type); Algorithm; Artificial neural network; Filter (signal processing); Computer vision; Programming language","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":[0.000103256,0.00008448151,0.0000881458,0.00003117902,0.0006093425,0.0002386398,0.001225303,0.00004134581,0.00005758178],"category_scores_gemma":[0.00002069621,0.00009039825,0.00004342479,0.00007919603,0.0001301604,0.001051084,0.0003194275,0.00007952945,0.0001535592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004881428,"about_ca_system_score_gemma":0.00004743466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005227974,"about_ca_topic_score_gemma":0.000004428395,"domain_scores_codex":[0.9993442,0.00001672705,0.0000611974,0.0003461088,0.00004416293,0.0001876132],"domain_scores_gemma":[0.9990088,0.00001641439,0.0001258953,0.0007090798,0.00005667641,0.00008307563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002007147,0.0001245891,0.01491151,0.00003146648,0.00004915055,0.0002824634,0.0003144866,0.000895004,0.001741632,0.8930752,0.08306087,0.005493502],"study_design_scores_gemma":[0.001928637,0.0001008456,0.01021967,0.000101223,0.00003000112,0.0000225272,0.0001366374,0.8504385,0.003449434,0.05588854,0.07681954,0.0008644093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09849081,0.00004010094,0.8750635,0.0005393433,0.000245965,0.00004129274,0.000002279367,0.000102938,0.02547379],"genre_scores_gemma":[0.9955962,0.00001972588,0.0005154407,0.0004108374,0.0000295349,9.630709e-8,7.630762e-7,0.000003627787,0.003423735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8971055,"threshold_uncertainty_score":0.4686631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05705792341172985,"score_gpt":0.1739792359229458,"score_spread":0.116921312511216,"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."}}