{"id":"W2577874402","doi":"10.5555/3042094.3042459","title":"A Bayesian inference based simulation approach for estimating fraction nonconforming of pipe spool welding processes","year":2016,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo method; Fraction (chemistry); Process (computing); Welding; Computer science; Schedule; Bayesian inference; Reliability (semiconductor); Inference; Process variable; Engineering; Bayesian probability; Reliability engineering; Mechanical engineering; Artificial intelligence; Mathematics; Statistics","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":[0.001271363,0.0002090999,0.0003517499,0.0003074072,0.0001373691,0.0001499458,0.0004116622,0.0001263936,0.0001649179],"category_scores_gemma":[0.01697193,0.0001395974,0.0001130462,0.0004566076,0.00008319059,0.0009196535,0.0000473929,0.00008209433,0.00001090653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007048355,"about_ca_system_score_gemma":0.0002651914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005533235,"about_ca_topic_score_gemma":0.000002152408,"domain_scores_codex":[0.9976274,0.00007131013,0.0009064187,0.0004944052,0.0006468024,0.0002536984],"domain_scores_gemma":[0.9894794,0.007794967,0.0005899256,0.0004106752,0.001635803,0.00008928828],"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":[0.00008113673,0.00003481514,0.001314581,0.00009821232,0.00000948737,1.729214e-7,0.0002241518,0.9467476,0.001208098,0.0002643476,0.000007420046,0.05001],"study_design_scores_gemma":[0.0005925908,0.00008702988,0.0003708609,0.000282309,0.00001995052,4.134491e-7,0.00008670131,0.9905924,0.002540366,0.005049345,0.0001770276,0.0002010221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005229277,0.000009061772,0.9934061,0.00007787783,0.0002079015,0.0005250354,0.00002040585,0.00009229469,0.0004320421],"genre_scores_gemma":[0.8339504,2.5062e-7,0.1657464,0.0000265666,0.0000950929,0.00003835566,0.000008391531,0.00001443813,0.0001200717],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8287212,"threshold_uncertainty_score":0.9913085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1283665555365754,"score_gpt":0.3805438260227215,"score_spread":0.2521772704861461,"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."}}