{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003637008,0.00009559283,0.0001706002,0.00002985119,0.00001021237,0.000002454059,0.0002660473,0.0001431454,0.000005077644],"category_scores_gemma":[0.0001156391,0.00009254491,0.00002994235,0.00008052272,0.00002234949,0.00000334814,0.00003346632,0.00008303813,7.722403e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001894393,"about_ca_system_score_gemma":0.00005913137,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0185956,"about_ca_topic_score_gemma":0.03185769,"domain_scores_codex":[0.9991658,0.00004188667,0.0003236574,0.0002069464,0.00008628904,0.0001753753],"domain_scores_gemma":[0.9992788,0.0000210334,0.00008235309,0.0004855254,0.00005105904,0.00008117758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005877008,0.0001548076,0.01803299,0.0002914081,0.00009874463,2.257008e-7,0.0003392659,0.1506145,0.808414,0.0139774,0.002153458,0.005864333],"study_design_scores_gemma":[0.001490935,0.00007680719,0.8911511,0.0000412655,0.00003336535,0.000004212572,0.0002651694,0.003765719,0.03249337,0.0001143065,0.07007177,0.0004919669],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8967096,0.001634836,0.09819267,0.0005441172,0.0001201397,0.0004036266,0.001459217,0.00001324974,0.0009225893],"genre_scores_gemma":[0.988622,0.000008929299,0.01070507,0.00007369636,0.0001089174,0.000006669441,0.0004280945,0.000007517688,0.00003911132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8731181,"threshold_uncertainty_score":0.9879397,"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."}}