{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002951648,0.0002282978,0.0004352655,0.0002362705,0.000376137,0.001099305,0.001781258,0.0001210321,0.00003658611],"category_scores_gemma":[0.007458786,0.0001633122,0.0001239589,0.0001788691,0.00003114838,0.0001844794,0.0002732336,0.000423098,0.0003239507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001791482,"about_ca_system_score_gemma":0.0001027657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006169808,"about_ca_topic_score_gemma":0.00000285233,"domain_scores_codex":[0.9973366,0.00003574935,0.0006471868,0.0003967456,0.001080304,0.0005033687],"domain_scores_gemma":[0.997343,0.0005175451,0.0002095012,0.0009332789,0.0003179502,0.0006787243],"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":[0.00006007979,0.00002369986,0.0002934716,0.000008423399,0.00003720542,0.00004225841,0.000561399,0.9536904,0.02113325,0.0006741474,0.01688405,0.006591626],"study_design_scores_gemma":[0.002383892,0.0002753762,0.02497254,0.0004697743,0.00009544643,0.0007697165,0.0007644493,0.8825898,0.006435489,0.001250098,0.07816797,0.001825467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3772974,0.000132213,0.6167428,0.000999816,0.002908209,0.0004905404,0.00001744031,0.00009770255,0.001313881],"genre_scores_gemma":[0.9861538,0.000002198603,0.01203938,0.0001086535,0.0007170602,0.0000232286,2.057895e-7,0.00002533367,0.0009301369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6088564,"threshold_uncertainty_score":0.9999377,"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."}}