{"id":"W1983263007","doi":"10.1016/j.strusafe.2005.10.002","title":"Analysis of approximations for multinormal integration in system reliability computation","year":2005,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Computation; Bivariate analysis; Simple (philosophy); Context (archaeology); Computer science; Conditional probability; Reliability engineering; Numerical integration; Algorithm; Mathematics; Applied mathematics; Statistics; Engineering; Machine learning","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":[0.007731828,0.0007604575,0.001441641,0.001118252,0.0007259051,0.001590998,0.002341446,0.00104623,0.002663786],"category_scores_gemma":[0.0398474,0.0009332596,0.0009352486,0.001184515,0.001417377,0.002644836,0.002042359,0.002670584,0.0003495025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002207881,"about_ca_system_score_gemma":0.001582792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01040817,"about_ca_topic_score_gemma":0.008691033,"domain_scores_codex":[0.9975969,0.001142455,0.0001171519,0.0002050075,0.000720612,0.0002178964],"domain_scores_gemma":[0.9797365,0.01647577,0.0007469455,0.001168433,0.00152537,0.0003470939],"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":[0.0001060006,0.0000309426,0.0009716916,0.00005554693,0.00003701197,0.00004029991,0.0001057955,0.9218992,0.0004663757,0.0599536,0.0005100789,0.01582342],"study_design_scores_gemma":[0.000001627487,0.000003777047,0.00003301175,0.000004192004,0.000002668338,0.000003622063,0.00000339544,0.9925155,0.00006901966,0.007267862,0.00009380775,0.000001501608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02329326,0.0003334313,0.9746337,0.0001454642,0.00003794914,0.00001606644,0.00002844727,0.0001880688,0.00132359],"genre_scores_gemma":[0.6554996,0.0004995081,0.3402862,0.0001636228,0.00008739204,0.0001260348,0.0001920691,0.0003572098,0.002788499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01040817,"threshold_uncertainty_score":0.04089028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04459091172091458,"score_gpt":0.3426442450358452,"score_spread":0.2980533333149306,"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."}}