{"id":"W7099848519","doi":"","title":"Generalized Quality Control for Optical Data Capture at Statistics Canada","year":2015,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Control (management); Quality (philosophy); Statistical process control; Data quality; Process control; Automatic identification and data capture","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":[0.02014181,0.0009284189,0.0008298181,0.004054733,0.002375835,0.003490715,0.002186838,0.0007790984,0.007912029],"category_scores_gemma":[0.04172334,0.0007879617,0.0009107409,0.006133509,0.002152902,0.001231924,0.00203242,0.001906762,0.002651199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02831078,"about_ca_system_score_gemma":0.0629712,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7756094,"about_ca_topic_score_gemma":0.7280189,"domain_scores_codex":[0.9723046,0.005893814,0.001176485,0.002697395,0.01645922,0.001468441],"domain_scores_gemma":[0.9422948,0.006860552,0.002960285,0.006038593,0.04124636,0.0005994318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006288787,0.0002356777,0.03517673,0.000735261,0.00024292,0.0002602877,0.0008469891,0.07847145,0.01373066,0.1302408,0.1158801,0.6235502],"study_design_scores_gemma":[0.000283888,0.0003019287,0.1119077,0.0004692097,0.0001341418,0.0002787268,0.0006624975,0.5449039,0.02736662,0.03847659,0.2747943,0.0004204272],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01292078,0.001064454,0.9493032,0.001352914,0.000175814,0.001698724,0.008391816,0.006899125,0.01819321],"genre_scores_gemma":[0.2343627,0.001378343,0.7206885,0.000707432,0.0001287714,0.002742461,0.01391231,0.0011674,0.02491223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2243906,"threshold_uncertainty_score":0.4514241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08684502614710958,"score_gpt":0.3319107995220021,"score_spread":0.2450657733748925,"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."}}