{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008502955,0.0002343267,0.0005084698,0.0002057386,0.000278739,0.00001174269,0.0002609553,0.0001495939,0.00002159035],"category_scores_gemma":[0.00009776684,0.0001662676,0.000306156,0.0002182248,0.001071028,0.0001543219,0.00004943953,0.0003296747,0.000007406464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357802,"about_ca_system_score_gemma":0.00002914163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000132939,"about_ca_topic_score_gemma":0.00001952495,"domain_scores_codex":[0.9983732,0.0001032184,0.0005091155,0.0003526385,0.0003770653,0.0002848288],"domain_scores_gemma":[0.9985878,0.0008747918,0.0002597525,0.00017771,0.00004761274,0.00005231614],"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":[0.002438705,0.0008145571,0.0007509205,0.0001694172,0.001360106,0.00002512315,0.005181582,0.9517099,0.0003879984,0.02653752,0.009110628,0.001513504],"study_design_scores_gemma":[0.003264536,0.004404175,0.004181746,0.0004678425,0.0007468298,0.00005785583,0.0005851778,0.7019242,0.0002083909,0.2574655,0.02613849,0.0005553174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1582319,0.001472165,0.7961099,0.04089798,0.0007095369,0.0007619259,0.00005135585,0.00004978555,0.00171547],"genre_scores_gemma":[0.9848871,0.0002449933,0.01286691,0.001729635,0.0001081522,0.00004833749,0.000005485364,0.00001855009,0.00009087083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8266552,"threshold_uncertainty_score":0.6780197,"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."}}