{"id":"W2168767705","doi":"10.1109/icpr.2000.906142","title":"Multivariate structural Bernoulli mixtures for recognition of handwritten numerals","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Numeral system; Multivariate statistics; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Bernoulli's principle; Natural language processing; Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001319684,0.0007124725,0.0007465986,0.0006125771,0.000329467,0.0008471237,0.000891049,0.001104679,0.002361253],"category_scores_gemma":[0.005542396,0.0005946128,0.0006943363,0.00103759,0.0007534419,0.001299778,0.0009466526,0.001277984,0.001107299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005559108,"about_ca_system_score_gemma":0.0005468852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001855034,"about_ca_topic_score_gemma":0.002383346,"domain_scores_codex":[0.9993634,0.000272839,0.00002853459,0.0001299492,0.0001633844,0.00004199408],"domain_scores_gemma":[0.9990311,0.0006501956,0.00009701258,0.0001066906,0.00008256846,0.00003250953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002711548,0.00006917313,0.0007415056,0.0001766792,0.00009896071,0.0001029759,0.0001322262,0.578635,0.0233552,0.06720725,0.003492377,0.3257176],"study_design_scores_gemma":[0.000004671298,0.00001153402,0.0003951553,0.000009933846,0.000008463517,0.00004856698,0.000004566317,0.9725415,0.00348189,0.02191668,0.001558604,0.00001830468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006347265,0.0006556172,0.9913258,0.0001501674,0.00004241123,0.00001909248,0.00005438485,0.000674094,0.0007311661],"genre_scores_gemma":[0.2795742,0.001744261,0.710429,0.000148313,0.0002319133,0.0001607028,0.0004691792,0.0003697491,0.006872533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002361253,"threshold_uncertainty_score":0.007899225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04536426820903318,"score_gpt":0.270901924394404,"score_spread":0.2255376561853709,"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."}}