{"id":"W2376318479","doi":"","title":"The Study of Automatic Notes Segmentation and Recognition Based on HMM","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Hidden Markov model; Segmentation; Artificial intelligence; Pattern recognition (psychology); Speech recognition","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006233747,0.0002771613,0.0009058452,0.0005805311,0.0003403401,0.001072717,0.0007061165,0.0007940737,0.001334747],"category_scores_gemma":[0.002949985,0.0004646143,0.0005757734,0.0009495015,0.0007229318,0.001556158,0.0003424805,0.0006333659,0.0004970708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004637453,"about_ca_system_score_gemma":0.0004462418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003596008,"about_ca_topic_score_gemma":0.001605341,"domain_scores_codex":[0.9994861,0.000134999,0.00003006128,0.0001564948,0.0001533366,0.00003899215],"domain_scores_gemma":[0.9974222,0.002071479,0.0001425594,0.0001251976,0.0001968416,0.00004180011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000773899,0.0001699176,0.007210489,0.001176573,0.0002824827,0.000901009,0.0006085188,0.2319151,0.1817534,0.07919359,0.002961284,0.4930538],"study_design_scores_gemma":[0.00001062925,0.00009672148,0.004632662,0.00001857913,0.00004069597,0.0002229161,0.00006161749,0.9694304,0.01579102,0.007690173,0.001978564,0.00002604597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09764834,0.00379372,0.8950476,0.0002971715,0.0001258786,0.00003029314,0.00008812046,0.0003003673,0.002668469],"genre_scores_gemma":[0.8177683,0.003730111,0.1660574,0.0001182757,0.0003961115,0.00006388896,0.0003774355,0.0001442894,0.01134423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003596008,"threshold_uncertainty_score":0.007150173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537792226709741,"score_gpt":0.2806114993917512,"score_spread":0.2552335771246538,"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."}}