{"id":"W3116488333","doi":"10.3390/w13010028","title":"Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":324,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"U.S. Geological Survey","keywords":"Uncertainty analysis; Calibration; Uncertainty quantification; Monte Carlo method; Computer science; Hydrological modelling; Bayesian probability; Environmental science; Data mining; Statistics; Mathematics; Machine learning; Climatology; Simulation; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001192081,0.00006580656,0.0001964371,0.0000193578,0.00002927082,0.000002286105,0.0000810064,0.00002010076,0.0002209944],"category_scores_gemma":[0.000007126171,0.00003443479,0.00003596121,0.000106295,0.0001953281,0.00009783463,0.0002045972,0.00004028699,0.00001962474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004017403,"about_ca_system_score_gemma":2.386301e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002248931,"about_ca_topic_score_gemma":0.0000419777,"domain_scores_codex":[0.9995154,0.00003076085,0.0001202698,0.000163708,0.00005028027,0.0001196032],"domain_scores_gemma":[0.9998892,0.000009495631,0.00001972802,0.00005941568,0.000001252899,0.00002092979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004448473,0.00003308101,0.7240658,0.0001725768,0.0001189432,0.000005278253,0.004741434,0.2675989,0.0006176204,0.00003089337,0.00106426,0.001506785],"study_design_scores_gemma":[0.001828044,0.0008256088,0.1141745,0.0002137972,0.001709113,0.000002937809,0.001386593,0.6334368,0.01752692,0.03330417,0.1941591,0.001432446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985941,0.005254548,0.0002098074,0.006317962,0.000004413321,0.00008172427,0.000001424637,0.00001077372,0.002178343],"genre_scores_gemma":[0.9878197,0.009022931,0.00007657493,0.00300039,0.000003013738,0.00000773735,0.00000211068,0.000001631216,0.00006591127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6098913,"threshold_uncertainty_score":0.2419733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446001934128183,"score_gpt":0.2230315451238151,"score_spread":0.2085715257825332,"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."}}