{"id":"W1968509973","doi":"10.1016/j.eswa.2010.12.093","title":"An empirical evaluation of attribute control charts for monitoring defects","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control chart; Computer science; Shewhart individuals control chart; Chart; Reliability engineering; Data mining; Statistical process control; Control limits; Process (computing); Statistics; EWMA chart; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.002617053,0.0001521873,0.0003362172,0.0001302046,0.000287819,0.0001183945,0.0004978396,0.00009646355,0.00001288446],"category_scores_gemma":[0.001573815,0.0001105286,0.0000494339,0.0004637126,0.0001074035,0.0003506935,0.0000180994,0.0001397433,0.00002392579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005583474,"about_ca_system_score_gemma":0.000147492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001806505,"about_ca_topic_score_gemma":0.000009355929,"domain_scores_codex":[0.9968296,0.0001374594,0.0006386787,0.0005494331,0.001587048,0.0002577225],"domain_scores_gemma":[0.9945199,0.001634383,0.0003858748,0.000864115,0.002386851,0.0002088987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004087947,0.001166829,0.3915329,0.0001532427,0.0001667646,0.000001594785,0.004213199,0.03622417,0.3697807,0.03393782,0.002292584,0.1601214],"study_design_scores_gemma":[0.01323989,0.001473864,0.1246876,0.0003000092,0.0004338924,0.00007733712,0.01100363,0.6121005,0.07969623,0.05008023,0.1046017,0.002305166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07940329,0.0002598839,0.9172824,0.00009866808,0.0004773422,0.002181508,0.00008307458,0.00006305855,0.0001507517],"genre_scores_gemma":[0.976908,0.000001085348,0.0162164,0.00001160866,0.0008051545,0.005988358,0.00001079413,0.00002283067,0.00003579579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.901066,"threshold_uncertainty_score":0.4507224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1698602375644312,"score_gpt":0.4993317718455054,"score_spread":0.3294715342810742,"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."}}