{"id":"W2800600340","doi":"10.3390/app8050654","title":"Deep Neural Networks for Document Processing of Music Score Images","year":2018,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Music and Audio Processing","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Universidad de Alicante","keywords":"Computer science; Digitization; Convolutional neural network; Artificial intelligence; Artificial neural network; Field (mathematics); Deep learning; Process (computing); Classifier (UML); Machine learning; Pattern recognition (psychology); Speech recognition; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003600296,0.000481047,0.0002829444,0.0004647931,0.000190375,0.000487112,0.0005749831,0.0005406971,0.002776076],"category_scores_gemma":[0.0009381794,0.000207279,0.0003106247,0.0006991189,0.0001782822,0.0006242617,0.000352529,0.0007583058,0.001034833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005854099,"about_ca_system_score_gemma":0.0004475713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006792782,"about_ca_topic_score_gemma":0.009005548,"domain_scores_codex":[0.999833,0.00002387628,0.00001201273,0.00003659551,0.00006701188,0.00002753426],"domain_scores_gemma":[0.9998145,0.00005726789,0.00002172129,0.00002912485,0.00006905732,0.000008523217],"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":[0.0003438035,0.0001444852,0.0007869112,0.0002553166,0.00007190945,0.0001656001,0.00005816006,0.2163727,0.09950259,0.004961589,0.006425091,0.670912],"study_design_scores_gemma":[0.00000739407,0.00006160717,0.0008883972,0.00001709462,0.00001739358,0.00003891672,0.00001476788,0.9650924,0.02807103,0.001977843,0.003802882,0.00001023997],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1801666,0.007218984,0.7892892,0.0007808189,0.000421826,0.0001854938,0.001293275,0.007631045,0.01301285],"genre_scores_gemma":[0.6900399,0.002572983,0.2903169,0.0002010078,0.0001058884,0.0001224122,0.00227024,0.0001602244,0.01421054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006792782,"threshold_uncertainty_score":0.01350647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942553726443466,"score_gpt":0.2716329150582529,"score_spread":0.2422073777938182,"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."}}