{"id":"W4405073432","doi":"10.1016/j.ejrh.2024.102095","title":"A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins","year":2024,"lang":"en","type":"article","venue":"Journal of Hydrology Regional Studies","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Korea Hydro and Nuclear Power","keywords":"Bayesian probability; Surface runoff; Environmental science; Drainage basin; Uncertainty quantification; Hydrology (agriculture); Geography; Water resource management; Computer science; Cartography; Geology; Artificial intelligence; Machine learning; Ecology; Geotechnical engineering","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":[0.003291693,0.0012297,0.001052038,0.002157514,0.0006656985,0.002022228,0.00207496,0.001248429,0.00145073],"category_scores_gemma":[0.006464684,0.0007857131,0.001671595,0.001284635,0.001264588,0.002144603,0.002361126,0.002067189,0.0002090289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001789754,"about_ca_system_score_gemma":0.003121147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009417383,"about_ca_topic_score_gemma":0.009471902,"domain_scores_codex":[0.9981716,0.0008714675,0.00009659692,0.0003250538,0.0004311407,0.0001042059],"domain_scores_gemma":[0.998189,0.0009619843,0.0002621809,0.0001320976,0.0003739938,0.00008069245],"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":[0.00001360626,0.00004365929,0.001259135,0.00009183784,0.0001183692,0.00009688351,0.0001072,0.8204641,0.001572866,0.1452069,0.0006416958,0.03038376],"study_design_scores_gemma":[0.00000405519,0.00001530927,0.0002029264,0.00002197855,0.00001708571,0.00002616227,0.00001336906,0.9660894,0.0002632718,0.03202399,0.001306105,0.0000163714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001733481,0.0001110923,0.9975029,0.00006454779,0.000008342633,0.00001988416,0.00003596232,0.00004907949,0.0004748469],"genre_scores_gemma":[0.3164123,0.0009619393,0.6798484,0.0001607986,0.0001270035,0.000518982,0.0003685553,0.0001117967,0.001490092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009417383,"threshold_uncertainty_score":0.01872516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06386001921397048,"score_gpt":0.2960135653124278,"score_spread":0.2321535460984573,"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."}}