{"id":"W2045220951","doi":"10.1007/s13735-012-0004-6","title":"Optical music recognition: state-of-the-art and open issues","year":2012,"lang":"en","type":"article","venue":"International Journal of Multimedia Information Retrieval","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":248,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fundação para a Ciência e a Tecnologia; University of Texas at Austin","keywords":"Computer science; Musical; Optical character recognition; Scheme (mathematics); State (computer science); Computer music; Character (mathematics); Artificial intelligence; Speech recognition; Natural language processing; Multimedia; Image (mathematics); Programming language; Visual arts","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102541,0.0001004775,0.0001775529,0.000260088,0.00004594452,0.0002931209,0.001343465,0.0000574006,0.0001091609],"category_scores_gemma":[0.000801734,0.00007541805,0.00008435958,0.0002146374,0.00009345719,0.006805247,0.0005151798,0.0002188557,0.00007922298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006435479,"about_ca_system_score_gemma":0.0001075748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004473377,"about_ca_topic_score_gemma":7.985251e-7,"domain_scores_codex":[0.9981337,0.00006550825,0.0007792226,0.00005953077,0.0008172241,0.0001448528],"domain_scores_gemma":[0.9974514,0.0001865498,0.0007498234,0.0001433907,0.001337098,0.0001317224],"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":[0.0001743909,0.0001582435,0.001857248,0.00001918818,0.0001306688,0.000004077449,0.003866477,0.000005061873,0.001004745,0.001142708,0.009822547,0.9818146],"study_design_scores_gemma":[0.006149016,0.0006989501,0.05439053,0.000811897,0.00007699087,0.002291268,0.0004410657,0.01135248,0.7034184,0.01460956,0.2049616,0.0007981792],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3875933,0.0004321618,0.578747,0.01261865,0.009718899,0.001191418,0.0001068287,0.0001319032,0.009459846],"genre_scores_gemma":[0.7537103,0.0002257624,0.244258,0.001240575,0.0004066604,0.000006140411,0.00001960866,0.000008614651,0.0001243683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9810165,"threshold_uncertainty_score":0.493364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.028366212247756,"score_gpt":0.2974230507446503,"score_spread":0.2690568384968943,"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."}}