{"id":"W603141552","doi":"","title":"Application of quality control in ICR data capture 2001 Canadian census of agriculture","year":2005,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Census; Quality assurance; Statistical process control; Quality (philosophy); Control (management); Data quality; Computer science; Process (computing); Automatic identification and data capture; Database; Operations research; Data science; Engineering; Operations management; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02425032,0.0004982686,0.0005130899,0.006987641,0.001905904,0.002236048,0.001892745,0.0005164554,0.001398009],"category_scores_gemma":[0.08205373,0.0004332243,0.0004242589,0.01447994,0.001073302,0.0005712765,0.00128396,0.0007828242,0.0004205658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03636677,"about_ca_system_score_gemma":0.03297941,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.944028,"about_ca_topic_score_gemma":0.9089671,"domain_scores_codex":[0.9448757,0.0150605,0.003654602,0.003385919,0.03154583,0.00147744],"domain_scores_gemma":[0.9160828,0.009434467,0.006671645,0.004743694,0.06233248,0.0007348614],"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":[0.0003144014,0.0001406174,0.3840239,0.001323284,0.0002920674,0.0001886128,0.003280662,0.0209438,0.004232268,0.01441279,0.05927558,0.5115721],"study_design_scores_gemma":[0.0000925206,0.0002241843,0.8420058,0.0004605424,0.0001407775,0.0001243003,0.001716932,0.03385974,0.01062972,0.001681783,0.1088774,0.0001863351],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2669882,0.006560693,0.4374038,0.01001956,0.0008744822,0.0212962,0.1368959,0.002554388,0.1174068],"genre_scores_gemma":[0.5960258,0.00304004,0.3484748,0.00117352,0.0001123025,0.004031497,0.03657281,0.0002941234,0.01027509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05597204,"threshold_uncertainty_score":0.2638606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861422828896759,"score_gpt":0.27196581602623,"score_spread":0.2533515877372625,"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."}}