{"id":"W2772538237","doi":"10.1002/aic.16045","title":"Multilevel Monte Carlo applied to chemical engineering systems subject to uncertainty","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Uncertainty quantification; Sampling (signal processing); Polynomial chaos; Latin hypercube sampling; Engineering; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002834579,0.0004605929,0.0009581302,0.0009862831,0.0004821463,0.0008637279,0.0008026627,0.0008663962,0.001187032],"category_scores_gemma":[0.01269058,0.0003885627,0.000798276,0.0008531002,0.0008305704,0.0008395507,0.001065821,0.001103258,0.0001196371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009723803,"about_ca_system_score_gemma":0.001024676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006903409,"about_ca_topic_score_gemma":0.004088893,"domain_scores_codex":[0.9984533,0.0008356079,0.00005748033,0.0001116897,0.0004495177,0.00009238647],"domain_scores_gemma":[0.9909537,0.007453865,0.0004574761,0.0004817442,0.0005338317,0.000119401],"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":[0.00004114995,0.00001352872,0.000802607,0.00004154251,0.00003907529,0.00003046811,0.00002397281,0.9767779,0.0008187016,0.01297056,0.0001044338,0.008336015],"study_design_scores_gemma":[0.000001600509,0.000006954026,0.00005699405,0.00000194407,0.00000251552,0.000002893883,0.000001075882,0.9980996,0.0002143414,0.001539797,0.00007066254,0.00000167639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04570467,0.000361788,0.9516392,0.0001754363,0.00003181784,0.00005315482,0.00005433685,0.0002306416,0.001749012],"genre_scores_gemma":[0.8167811,0.0003004768,0.1820389,0.00008000378,0.00004225684,0.0001474705,0.00007896335,0.0000636066,0.0004672215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006903409,"threshold_uncertainty_score":0.01499081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09056344298251777,"score_gpt":0.3369848557918908,"score_spread":0.246421412809373,"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."}}