{"id":"W2951828725","doi":"10.48550/arxiv.1409.5317","title":"A Bayesian model for recognizing handwritten mathematical expressions","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Parsing; Parse tree; Probabilistic logic; Bayesian network; Artificial intelligence; Ambiguity; Tree (set theory); Identification (biology); Pattern recognition (psychology); Natural language processing; Bayesian probability; Machine learning; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000539439,0.0004331988,0.0005676982,0.0003855382,0.0002856095,0.0002480796,0.002188361,0.0005438748,0.00004063411],"category_scores_gemma":[0.0001715384,0.0004762119,0.0004392809,0.0002876137,0.000107108,0.0004287208,0.001950465,0.0006003749,0.00008262365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465971,"about_ca_system_score_gemma":0.0002092535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008974644,"about_ca_topic_score_gemma":0.000007719916,"domain_scores_codex":[0.9974192,0.0001438469,0.0003751066,0.001397946,0.0001276931,0.0005362005],"domain_scores_gemma":[0.9972062,0.0003429935,0.0003202622,0.001507634,0.0003167692,0.0003061532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001329145,0.0009396863,0.0001453462,0.001821293,0.0004416132,0.0002213836,0.002253006,0.1232517,0.001212638,0.8219054,0.01595039,0.03172465],"study_design_scores_gemma":[0.0002573838,0.00002775791,0.000002125282,0.0002900993,0.00004616285,0.00000435246,0.00001426762,0.5924052,0.0006442704,0.4058181,0.000141293,0.0003489108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003252779,0.00001677152,0.9910195,0.0002354767,0.00016689,0.0008645005,0.00004593124,0.001053504,0.003344651],"genre_scores_gemma":[0.7663836,0.0000438862,0.2310944,0.0002209749,0.00008177883,0.00002915285,0.00002837084,0.00004034278,0.002077526],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7631308,"threshold_uncertainty_score":0.999769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09535052164430832,"score_gpt":0.2244525857650222,"score_spread":0.1291020641207139,"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."}}