{"id":"W2765217769","doi":"10.1145/3144749.3144751","title":"PatternFinder","year":2017,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Centro de Investigação em Biomedicina; Social Sciences and Humanities Research Council of Canada; Fonds de Recherche du Québec-Société et Culture; Helsingin Yliopisto","keywords":"Polyphony; Melody; Computer science; Python (programming language); Music information retrieval; Pop music automation; Relevance (law); Music theory; Software; Notation; Musicology; The Renaissance; Musical notation; Musical; Musical composition; Programming language; Art; Linguistics; Visual arts; Literature","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000447175,0.00002491491,0.00002675295,0.000007059337,0.0002481798,0.0004446365,0.0006965945,0.00000999789,0.0000743115],"category_scores_gemma":[0.000008007408,0.00001842457,0.00001081504,0.000007520435,0.00001464253,0.0004194033,0.0001833171,0.00002162932,0.0001304434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001928237,"about_ca_system_score_gemma":0.00001055617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001370647,"about_ca_topic_score_gemma":0.000002572837,"domain_scores_codex":[0.9997448,0.00000204385,0.00003043538,0.00009364579,0.00005693133,0.00007216151],"domain_scores_gemma":[0.999487,0.000003464529,0.00002917416,0.0004483882,0.00001046037,0.00002145544],"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":[1.874827e-7,0.00001018835,0.008931353,0.000004597062,0.00000275576,0.00001082556,0.0001714229,7.602026e-7,0.0003983183,0.09449007,0.01959565,0.8763838],"study_design_scores_gemma":[0.0009796547,0.00004517469,0.537195,0.00006436677,0.000004327343,0.00004290049,0.00002417054,0.0553919,0.04630304,0.09620216,0.2629802,0.0007671272],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005502826,0.000005990417,0.7979863,0.005725502,0.0001515833,0.000008402764,2.1771e-8,0.00004662506,0.1905727],"genre_scores_gemma":[0.9731804,5.477313e-7,0.01991385,0.002645142,0.00004387657,6.510471e-7,2.481358e-8,0.000001042778,0.004214445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9676776,"threshold_uncertainty_score":0.428764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03132283491685223,"score_gpt":0.2779136166739687,"score_spread":0.2465907817571165,"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."}}