{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003584355,0.001023856,0.001844589,0.004606707,0.0003760242,0.002056713,0.00153779,0.001282313,0.004406305],"category_scores_gemma":[0.0151779,0.0004979562,0.001106206,0.007002529,0.0007517579,0.00302193,0.0009253721,0.001714714,0.001613798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00137615,"about_ca_system_score_gemma":0.004642576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003444567,"about_ca_topic_score_gemma":0.004162987,"domain_scores_codex":[0.9984258,0.0004114468,0.0002810591,0.0002344409,0.0005895593,0.00005774296],"domain_scores_gemma":[0.9886392,0.007686048,0.0009056036,0.0002295253,0.002407338,0.0001322675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006442975,0.00002682662,0.0005197392,0.05540663,0.00046393,0.0001166031,0.0001546412,0.002942515,0.0005275488,0.01559097,0.1305657,0.7936205],"study_design_scores_gemma":[0.000009187465,0.0000480741,0.001168936,0.01734017,0.000496932,0.0003269646,0.0001101393,0.0006333016,0.0003639517,0.009488183,0.969963,0.0000511508],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002041265,0.9932699,0.001609315,0.001825825,0.001134707,0.00001629506,0.0002362391,0.00003022739,0.00167341],"genre_scores_gemma":[0.001821695,0.9955345,0.0008506182,0.0005659704,0.0007058763,0.00001740992,0.0001799856,0.00001157077,0.0003123854],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004606707,"threshold_uncertainty_score":0.01895612,"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."}}