{"id":"W2593467507","doi":"10.1002/aic.15702","title":"A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Uncertainty quantification; Propagation of uncertainty; Multivariate statistics; Mathematical optimization; Computer science; Applied mathematics; Scale (ratio); Algorithm; Biological system; Mathematics; Monte Carlo method; Statistics; Physics; Machine learning","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":[0.003620875,0.001098278,0.001076768,0.001504263,0.0004103921,0.001093163,0.0009943316,0.001030669,0.001175394],"category_scores_gemma":[0.01024632,0.0004500467,0.001086481,0.0009158956,0.000811032,0.002197681,0.001661023,0.001296551,0.0002199018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007690588,"about_ca_system_score_gemma":0.001078734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001574875,"about_ca_topic_score_gemma":0.001031682,"domain_scores_codex":[0.9979712,0.0007736477,0.0001283419,0.0001836124,0.0008508299,0.00009227602],"domain_scores_gemma":[0.994539,0.003710625,0.0004049156,0.000512169,0.0007690116,0.00006435435],"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":[0.0001651696,0.00006280963,0.0006619291,0.0003084883,0.0001235625,0.00005982949,0.0001193918,0.7831448,0.01052321,0.06251729,0.000486273,0.1418272],"study_design_scores_gemma":[0.000006685805,0.00003887369,0.0001731258,0.00001529436,0.000009593921,0.00002661641,0.00001184274,0.9899888,0.003402388,0.00576131,0.0005503932,0.00001503923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01333285,0.0006179623,0.9843002,0.00008286629,0.00002157989,0.00003810223,0.00004238135,0.0001900486,0.001374031],"genre_scores_gemma":[0.4244196,0.001369344,0.5726978,0.00006914037,0.00006599333,0.0001605665,0.0002006655,0.0001744568,0.0008423929],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003620875,"threshold_uncertainty_score":0.01914924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2184717000214263,"score_gpt":0.4538826030049917,"score_spread":0.2354109029835655,"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."}}