{"id":"W2087738591","doi":"10.1142/s0129054108005528","title":"IDENTIFYING RHYTHMS IN MUSICAL TEXTS","year":2008,"lang":"en","type":"article","venue":"International Journal of Foundations of Computer Science","topic":"Music and Audio Processing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Rhythm; Substring; Duration (music); Sequence (biology); Musical; Speech recognition; Computer science; Mathematics; Literature; Data structure; Art; Physics; Acoustics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005169762,0.0006583724,0.0006837037,0.004131425,0.0007763344,0.001408649,0.0007493076,0.000778846,0.002643292],"category_scores_gemma":[0.006269074,0.0004210634,0.0005464462,0.002979552,0.0006216416,0.00295071,0.001580254,0.0008506954,0.00247213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003743312,"about_ca_system_score_gemma":0.0004368184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009249626,"about_ca_topic_score_gemma":0.0008452933,"domain_scores_codex":[0.9989823,0.0001465855,0.0001528133,0.0003664091,0.0002520698,0.0000998065],"domain_scores_gemma":[0.9974016,0.001144988,0.0005733144,0.0003563244,0.0003605494,0.0001633035],"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":[0.001066006,0.0001399529,0.01762179,0.0009961472,0.00006901035,0.001049203,0.002044381,0.009073498,0.1267782,0.01303781,0.006135336,0.8219886],"study_design_scores_gemma":[0.000340711,0.001761534,0.06603628,0.0007125591,0.0003785828,0.006297053,0.005749337,0.4664936,0.1408257,0.1800053,0.1311245,0.0002748549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3673026,0.004814455,0.6122082,0.0007004914,0.0002936709,0.0004150585,0.003799337,0.003681351,0.006784939],"genre_scores_gemma":[0.5496507,0.00234975,0.4373046,0.0001140855,0.0003938873,0.0002067793,0.00568147,0.0003299219,0.003968873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004131425,"threshold_uncertainty_score":0.008842647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04141102091982669,"score_gpt":0.324506851406212,"score_spread":0.2830958304863854,"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."}}