{"id":"W1507038277","doi":"10.1016/j.ejor.2004.09.027","title":"The non-central chi-square chart with two-stage samplings","year":2004,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Chart; Statistics; Statistic; Stage (stratigraphy); Square (algebra); Chi-square test; Mathematics; \\bar x and R chart; Sample (material); Sampling (signal processing); Control chart; Computer science; Control limits; Process (computing); Geometry; Geology; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01124051,0.0001190832,0.0001740851,0.0002021245,0.001118036,0.001041477,0.001239548,0.00001205938,0.000183847],"category_scores_gemma":[0.008712234,0.00006099894,0.00006358074,0.0006664437,0.0004123749,0.000652757,0.0001299124,0.0009055335,0.0002966665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001212152,"about_ca_system_score_gemma":0.0006385563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007781925,"about_ca_topic_score_gemma":0.00001687772,"domain_scores_codex":[0.9932629,0.0006744691,0.0007718099,0.0002667591,0.004517013,0.0005069878],"domain_scores_gemma":[0.9941038,0.002012389,0.0002191026,0.0002894872,0.003042883,0.00033229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003018921,0.0004548907,0.02320163,0.00002981219,0.0001849211,0.003934773,0.005302282,0.6199856,0.005243395,0.14853,0.006509897,0.1836039],"study_design_scores_gemma":[0.01379392,0.00650346,0.6533763,0.0008982943,0.00002767506,0.001517437,0.01192968,0.00845702,0.006864554,0.07442379,0.2210475,0.001160404],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2815872,0.0003395559,0.6924104,0.008988638,0.0005503428,0.0003179126,0.00003001903,0.00001183754,0.01576407],"genre_scores_gemma":[0.9754744,0.00002986741,0.02204794,0.0000728544,0.00083343,0.000001205151,0.000001334412,0.00002013851,0.001518837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6938872,"threshold_uncertainty_score":0.9999955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2636473634867568,"score_gpt":0.5035383292571186,"score_spread":0.2398909657703618,"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."}}