{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001016781,0.0001072114,0.00009913028,0.00008348779,0.0005483298,0.00004824484,0.0003886718,0.00002462693,0.000001100865],"category_scores_gemma":[0.000001469113,0.0000870553,0.00002966435,0.0003953038,0.00006627854,0.0001190181,0.00008913538,0.00007511053,0.00001271518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002443002,"about_ca_system_score_gemma":0.00002894571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008996303,"about_ca_topic_score_gemma":0.00000265001,"domain_scores_codex":[0.9991009,0.00004272172,0.0002774508,0.0002948124,0.0001715234,0.0001126298],"domain_scores_gemma":[0.9988604,0.0004433114,0.0001538305,0.0003785103,0.0001242445,0.0000396903],"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":[0.000003330375,0.0007195962,0.0002752459,0.00001353943,0.00001533362,7.250713e-7,0.000665865,0.002222292,0.001665963,0.006375392,0.0002386815,0.9878041],"study_design_scores_gemma":[0.001953269,0.0009772831,0.03063878,0.00005700172,0.00003626334,0.00006978113,0.0001737574,0.8631918,0.01794909,0.06510767,0.01919079,0.000654506],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05779254,0.00002110638,0.9401034,0.0006018396,0.000006269533,0.001214261,0.000004352868,0.000175403,0.00008088103],"genre_scores_gemma":[0.5080014,0.00001401423,0.4907022,0.0002145716,0.00002163322,0.001020214,0.00001126577,0.000006647344,0.000008055928],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9871495,"threshold_uncertainty_score":0.4217365,"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."}}