{"id":"W4381855979","doi":"10.1002/cjce.25015","title":"Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Linearization; Uncertainty quantification; Range (aeronautics); Propagation of uncertainty; Monte Carlo method; Sensitivity analysis; Uncertainty analysis; Nonlinear system; Computer science; Estimation theory; Mathematics; Algorithm; Statistics; Engineering; Machine learning; Simulation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.002870368,0.002009485,0.001999597,0.004152062,0.0004499217,0.002449588,0.002400613,0.001712663,0.002539532],"category_scores_gemma":[0.005434495,0.0009493161,0.001500207,0.004727929,0.001366635,0.0032065,0.001362517,0.001613809,0.001097808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001285794,"about_ca_system_score_gemma":0.002280603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003236954,"about_ca_topic_score_gemma":0.0017825,"domain_scores_codex":[0.9984565,0.0003404237,0.0001975532,0.0003054143,0.0006400766,0.00006000924],"domain_scores_gemma":[0.9948607,0.003708747,0.0003379429,0.0001753149,0.0008712116,0.0000459418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005517347,0.00008647748,0.0008014333,0.02884576,0.0004103937,0.0002538222,0.0002006543,0.03197523,0.002074769,0.05934253,0.01476376,0.86119],"study_design_scores_gemma":[0.0000179588,0.0001938783,0.002011327,0.02329043,0.0007846704,0.00129899,0.0002425016,0.0232693,0.006770264,0.07340712,0.8684711,0.0002423631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000632636,0.9652112,0.02912652,0.0005683493,0.0004211384,0.00002881391,0.0001085258,0.00007350869,0.003829455],"genre_scores_gemma":[0.00871968,0.9808023,0.009026706,0.0002330806,0.0004637364,0.00003584969,0.0001111365,0.00002661632,0.000580898],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004152062,"threshold_uncertainty_score":0.01518011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05221063776025226,"score_gpt":0.2769929243615742,"score_spread":0.224782286601322,"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."}}