{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003072552,0.0002162616,0.0004432872,0.000366435,0.000176112,0.0002153779,0.0006798701,0.0001060305,0.00002045884],"category_scores_gemma":[0.0104136,0.0001360312,0.0001388013,0.0008992798,0.0001852422,0.0002546202,0.00006608677,0.0005112743,0.00001007307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000227005,"about_ca_system_score_gemma":0.000549397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003448781,"about_ca_topic_score_gemma":0.00005411095,"domain_scores_codex":[0.9977277,0.00005439427,0.0008566451,0.0002233527,0.0007121181,0.0004257962],"domain_scores_gemma":[0.9975672,0.001046853,0.0002222295,0.0003286068,0.0004081354,0.0004269741],"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":[0.000009217188,0.000003135486,0.0001785512,0.0002997594,0.0000601243,0.00002565742,0.001063585,0.9769402,0.002840854,0.001902776,0.01132103,0.005355107],"study_design_scores_gemma":[0.0001673085,0.0000414668,0.00006841237,0.001169447,0.00006226562,0.0001110036,0.0001405061,0.9769762,0.001086589,0.01776124,0.00219012,0.0002254586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8087659,0.02671839,0.1395048,0.02028949,0.002724044,0.001191645,0.00005991918,0.0003624318,0.0003832932],"genre_scores_gemma":[0.9959108,0.000193061,0.003238718,0.0002147131,0.0001901379,0.00001276471,0.000002789434,0.00002774767,0.0002092358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1871449,"threshold_uncertainty_score":0.9979221,"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."}}