{"id":"W2395667131","doi":"10.5072/zenodo.244224","title":"MAP Adaptation to Improve Optical Music Recognition of Early Music Documents Using Hidden Markov Models.","year":2007,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Hidden Markov model; Computer science; Adaptation (eye); Speech recognition; Maximum a posteriori estimation; Ground truth; Recall; Baseline (sea); A priori and a posteriori; Artificial intelligence; Precision and recall; Markov model; Pattern recognition (psychology); Machine learning; Markov chain; Maximum likelihood; Mathematics; Statistics","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":[0.001813698,0.0009127733,0.0007993196,0.001142773,0.0003525231,0.0007044563,0.0009857325,0.0009772466,0.001670263],"category_scores_gemma":[0.006816468,0.0004198622,0.0007451774,0.0008798481,0.0003680334,0.001092232,0.0007731093,0.001344234,0.002352374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003798197,"about_ca_system_score_gemma":0.000474695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004465284,"about_ca_topic_score_gemma":0.005284183,"domain_scores_codex":[0.9989772,0.0003702271,0.00005212971,0.0002805796,0.000249889,0.00007000811],"domain_scores_gemma":[0.9970928,0.001736709,0.0001552332,0.0004114849,0.0005388026,0.0000649151],"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":[0.0005047576,0.0002082634,0.004534299,0.0001862621,0.0002741215,0.0001793936,0.0002332126,0.03185792,0.05091789,0.0008341267,0.006743832,0.9035259],"study_design_scores_gemma":[0.00004642429,0.0002088817,0.007113162,0.0000256034,0.00011899,0.000340606,0.0001142597,0.9175214,0.06481148,0.002879775,0.00674822,0.00007117722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06190391,0.0009747701,0.9194209,0.0002153608,0.0002353759,0.0001392836,0.0004129472,0.01447356,0.002223796],"genre_scores_gemma":[0.458206,0.0004289468,0.5314139,0.0003270717,0.0001894832,0.0002169979,0.001318523,0.0005987047,0.007300394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004465284,"threshold_uncertainty_score":0.009591877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05925206031133519,"score_gpt":0.2722022497220632,"score_spread":0.212950189410728,"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."}}