{"id":"W2344221280","doi":"10.1080/00224065.2016.11918155","title":"Assessing Binary Measurement Systems: A Cost-Effective Alternative to Complete Verification","year":2016,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Binary number; Computer science; Standard error; Standard deviation; Observational error; Measure (data warehouse); Statistics; Reliability engineering; Mathematics; Data mining; Arithmetic; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.008100729,0.0001422296,0.0005471017,0.000779296,0.0001289979,0.0001305033,0.0008624882,0.0001113679,0.00001244955],"category_scores_gemma":[0.03390061,0.00008470286,0.00007397782,0.0008698872,0.0002004752,0.0007250927,0.0001469635,0.0002697139,0.00008318079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000752883,"about_ca_system_score_gemma":0.0001084415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008266508,"about_ca_topic_score_gemma":0.00000309997,"domain_scores_codex":[0.9954055,0.0006380423,0.001393799,0.0003495257,0.00194249,0.0002706217],"domain_scores_gemma":[0.9922521,0.002447602,0.001457551,0.0004311869,0.003267724,0.0001438072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00018666,0.0001181247,0.003097173,0.00001869366,0.00006916514,0.00004594401,0.0002157184,0.0004428429,0.1896469,0.03934395,0.0003340028,0.7664808],"study_design_scores_gemma":[0.003834337,0.002506291,0.09223157,0.001825359,0.00008245181,0.0003238513,0.01195159,0.001033177,0.06456815,0.7465408,0.07416876,0.0009336185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09893128,0.0001966293,0.8919092,0.007395696,0.0008943683,0.000472891,0.00001467523,0.00003792956,0.0001473056],"genre_scores_gemma":[0.9866092,0.000007852869,0.0130842,0.00006863149,0.0001422765,0.00005481463,8.670821e-8,0.00001023124,0.00002272449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8876779,"threshold_uncertainty_score":0.9742373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4599384741626776,"score_gpt":0.5374695245883914,"score_spread":0.07753105042571373,"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."}}