{"id":"W191358350","doi":"10.1007/978-94-007-5134-7_9","title":"Enhanced Monte Carlo for Reliability-Based Design and Calibration","year":2012,"lang":"en","type":"book-chapter","venue":"Computational methods in applied sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Monte Carlo method; Quantile; Reliability (semiconductor); Parametric statistics; Range (aeronautics); Margin (machine learning); Calibration; Random variable; Limit (mathematics); Computer science; Set (abstract data type); Algorithm; Applied mathematics; Mathematical optimization; Mathematics; Reliability engineering; Statistical physics; Statistics; Engineering; Physics; Power (physics)","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":[0.00212513,0.001206844,0.001462398,0.0009532039,0.0002834236,0.001276052,0.0020372,0.0015816,0.007008274],"category_scores_gemma":[0.006350402,0.0009819013,0.0009861543,0.001156716,0.001020733,0.001665535,0.001310296,0.002382878,0.002247869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028173,"about_ca_system_score_gemma":0.0008095348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00136305,"about_ca_topic_score_gemma":0.001655114,"domain_scores_codex":[0.9984599,0.0006040647,0.00005505758,0.0001493441,0.000688754,0.00004280379],"domain_scores_gemma":[0.9975579,0.00152857,0.0001197462,0.0004209298,0.0003397784,0.00003305231],"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":[0.00004671777,0.00004418327,0.0001534059,0.0001924441,0.00007664481,0.00004735885,0.00004307075,0.7168146,0.002468368,0.1520149,0.003972662,0.1241256],"study_design_scores_gemma":[0.000008493678,0.0000137437,0.00006650633,0.00003607571,0.00001617296,0.00004357297,0.000002735625,0.9176825,0.001568773,0.07056427,0.009983135,0.00001403724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002995014,0.0004808051,0.9968907,0.0000363385,0.00002788093,0.00001006111,0.00001735104,0.0002241631,0.002013114],"genre_scores_gemma":[0.0647215,0.001596895,0.9238018,0.000156292,0.0001552672,0.000218143,0.0001937584,0.0004445108,0.00871193],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007008274,"threshold_uncertainty_score":0.02344507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2195547630484959,"score_gpt":0.4273084096360161,"score_spread":0.2077536465875203,"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."}}