{"id":"W126006283","doi":"10.1080/00224065.2000.11979996","title":"Monitoring Processes with Highly Censored Data","year":2000,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Censoring (clinical trials); Control chart; Computer science; Reliability engineering; Statistical process control; Statistics; Process (computing); Shewhart individuals control chart; Mathematics; Engineering; EWMA chart","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01297657,0.0007784594,0.001309914,0.001621413,0.0006864046,0.002443701,0.001513698,0.001513454,0.0009760467],"category_scores_gemma":[0.05082906,0.0003272662,0.0008582383,0.00169411,0.001722936,0.002491361,0.001428576,0.001400612,0.0002240944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006831286,"about_ca_system_score_gemma":0.001160499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001662918,"about_ca_topic_score_gemma":0.0008909659,"domain_scores_codex":[0.9918224,0.003921974,0.0005918802,0.001284773,0.002004521,0.000374394],"domain_scores_gemma":[0.9610924,0.02592127,0.006213942,0.003329695,0.002993599,0.0004490124],"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":[0.0008293361,0.0002068152,0.01866291,0.0003231263,0.0003042905,0.0007545522,0.0007186651,0.5767879,0.01229533,0.1627537,0.001834024,0.2245293],"study_design_scores_gemma":[0.00004772003,0.0001942842,0.002878695,0.000049994,0.00004934365,0.0001645165,0.0000464179,0.9357567,0.006880613,0.05250071,0.001368615,0.00006237109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0306024,0.0002385309,0.9679625,0.0001189301,0.00003831486,0.00004253678,0.00005331961,0.0003870335,0.0005563364],"genre_scores_gemma":[0.829596,0.0005348568,0.168102,0.0001052736,0.0001345155,0.0001473057,0.0002057723,0.00006200606,0.001112269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01297657,"threshold_uncertainty_score":0.06862748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2433913843936824,"score_gpt":0.4970511335271001,"score_spread":0.2536597491334177,"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."}}