{"id":"W1986382134","doi":"10.1080/00207543.2011.578165","title":"Capability index of a complex-product machining process","year":2011,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Machining; Process capability; Fixture; Process (computing); Taguchi methods; Process capability index; Quality (philosophy); Product (mathematics); Engineering; Index (typography); Computer science; Manufacturing engineering; Reliability engineering; Industrial engineering; Mechanical engineering; Work in process; Mathematics; Operations management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105576,0.0005655842,0.0003141653,0.001409538,0.0002067652,0.001019446,0.000600105,0.0004297241,0.0009625444],"category_scores_gemma":[0.003274316,0.0002198196,0.0004555185,0.001259491,0.0005344531,0.001154808,0.0005344129,0.0004317231,0.0002080731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009796522,"about_ca_system_score_gemma":0.000673583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001930271,"about_ca_topic_score_gemma":0.0008134054,"domain_scores_codex":[0.9990816,0.00008750251,0.00005456115,0.000210921,0.0005115758,0.0000538364],"domain_scores_gemma":[0.9981536,0.000831511,0.0004821724,0.0001264569,0.0003529678,0.00005331797],"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":[0.00017856,0.0001053569,0.009812664,0.0003811173,0.00006079429,0.0002539977,0.0002896295,0.7318318,0.1043699,0.01580109,0.0006206898,0.1362944],"study_design_scores_gemma":[0.000006490264,0.000158643,0.005392259,0.00001061582,0.00002337345,0.0001164191,0.00001946388,0.9636557,0.02584596,0.00334066,0.001392731,0.00003779928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1702114,0.0004915513,0.8196462,0.00009505522,0.00002223377,0.0001010867,0.0001280165,0.0004064817,0.008898099],"genre_scores_gemma":[0.9408898,0.0002471723,0.05699,0.00001359812,0.000010047,0.00008990883,0.0001159484,0.00004112438,0.00160243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001930271,"threshold_uncertainty_score":0.007107854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1365963618035683,"score_gpt":0.3778159958256035,"score_spread":0.2412196340220353,"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."}}