{"id":"W3186552400","doi":"10.1029/2020wr029149","title":"Uncertainty Analysis for Hydrological Models With Interdependent Parameters: An Improved Polynomial Chaos Expansion Approach","year":2021,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Soil and Water Assessment Tool; Monte Carlo method; Uncertainty analysis; Probabilistic logic; Propagation of uncertainty; Uncertainty quantification; Computer science; Principal component analysis; Mathematical optimization; Computation; Mathematics; Applied mathematics; Algorithm; Statistics; Streamflow","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":[],"consensus_categories":[],"category_scores_codex":[0.008442948,0.00023809,0.0005553007,0.0007123937,0.000400663,0.0007694509,0.001247777,0.0001823451,0.0001116213],"category_scores_gemma":[0.0007461389,0.0001203091,0.0002673841,0.00123888,0.0003239648,0.0002705346,0.0005230304,0.0004761696,0.00002472761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071316,"about_ca_system_score_gemma":0.00006795253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000161207,"about_ca_topic_score_gemma":0.00007705575,"domain_scores_codex":[0.9939399,0.001321108,0.0005820097,0.001308483,0.001910042,0.0009385023],"domain_scores_gemma":[0.9965484,0.0009705043,0.00006145574,0.001240931,0.0008177841,0.0003609865],"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":[0.001001593,0.0003373636,0.0002997912,0.00002005148,0.0002923434,0.00004696404,0.006151716,0.9811584,0.005798356,0.00009985349,0.000209993,0.004583555],"study_design_scores_gemma":[0.0007198004,0.0006302983,0.000146883,0.000008609682,0.00006604577,0.00001652479,0.002686988,0.9870074,0.002960708,0.00388652,0.00163043,0.0002398302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5671748,0.000049714,0.431468,0.0002249788,0.00003426278,0.0004023359,0.00002078455,0.0000491429,0.0005760267],"genre_scores_gemma":[0.9779683,0.00000350748,0.01988274,0.00004752854,0.0001230607,0.0001940589,0.00009278958,0.00002565364,0.001662413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4115852,"threshold_uncertainty_score":0.7419833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2507717554446942,"score_gpt":0.3878037842030334,"score_spread":0.1370320287583393,"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."}}