{"id":"W3001537166","doi":"10.1002/qre.2623","title":"Confidence limits for compliance testing using mixed acceptance criteria","year":2020,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Moratuwa; National Research Council Sri Lanka; Faculty of Graduate Studies and Research, University of Regina","keywords":"Confidence interval; Statistics; Parametric statistics; Limit (mathematics); Sample (material); Acceptance testing; Mathematics; Quality (philosophy); Econometrics; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001344351,0.000149279,0.0002780735,0.00004256501,0.0001231241,0.0002082978,0.0004939045,0.00006071378,0.00003710218],"category_scores_gemma":[0.0572038,0.000138508,0.000054768,0.0002549744,0.00008766747,0.0004416672,0.0001145949,0.0001761658,0.000007375348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006207058,"about_ca_system_score_gemma":0.00003340597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001306826,"about_ca_topic_score_gemma":6.717766e-7,"domain_scores_codex":[0.9977811,0.00005569039,0.0007304253,0.0005888264,0.0006292199,0.0002147677],"domain_scores_gemma":[0.9937701,0.00488644,0.0001848434,0.0002068584,0.0007785498,0.0001732409],"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":[0.000669261,0.0002241398,0.0493985,0.001505626,0.00010512,0.00001657977,0.002460615,0.5628316,0.1546011,0.1552332,0.0007959852,0.07215829],"study_design_scores_gemma":[0.0002900676,0.00003981756,0.03251811,0.00007696398,0.000005363145,0.000003784169,0.0001273578,0.9322439,0.001799886,0.03084894,0.001820441,0.0002253087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1078751,0.00005267216,0.8889342,0.001784995,0.000911147,0.0001583688,0.0001028443,0.00008023794,0.0001004477],"genre_scores_gemma":[0.7749391,0.000002135606,0.2245674,0.0001773184,0.0002684795,0.00001205367,0.000003071744,0.000009688461,0.00002074672],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.667064,"threshold_uncertainty_score":0.9507378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5045208900524006,"score_gpt":0.5088491805428861,"score_spread":0.004328290490485442,"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."}}