{"id":"W2472611738","doi":"10.1175/jhm-d-15-0236.1","title":"Evaluation of Uncertainties in Input Data and Parameters of a Hydrological Model Using a Bayesian Framework: A Case Study of a Snowmelt–Precipitation-Driven Watershed","year":2016,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Snowmelt; Snow; Environmental science; Precipitation; Bayesian inference; Watershed; Hydrological modelling; Bayesian probability; Computer science; Surface runoff; Calibration; Soil and Water Assessment Tool; Meteorology; Statistics; Streamflow; Climatology; Drainage basin; Machine learning; Mathematics","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":[0.003420841,0.0001420605,0.0005894661,0.000325836,0.00004298112,0.00000297978,0.0003362536,0.0001259037,0.00006758091],"category_scores_gemma":[0.0006280091,0.00009148671,0.00004913251,0.0002191826,0.0005850272,0.00031668,0.0004854813,0.0001529771,4.863796e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007637054,"about_ca_system_score_gemma":0.00002499326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004379768,"about_ca_topic_score_gemma":0.0004483018,"domain_scores_codex":[0.9973774,0.0007880702,0.00087922,0.0002673396,0.0004707258,0.0002172546],"domain_scores_gemma":[0.9984105,0.0003750532,0.0007679569,0.0003310216,0.00006841804,0.00004710393],"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":[0.0007372944,0.001173966,0.5162315,0.00003752916,0.0006825458,0.0002975721,0.01734135,0.4448667,0.01314642,0.00002194301,0.00002931362,0.005433868],"study_design_scores_gemma":[0.006058321,0.005214756,0.02777597,0.0001135958,0.001358479,0.0005756903,0.004959193,0.9243506,0.0006290634,0.02870579,0.000003455808,0.0002551197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940399,0.00005431033,0.005188349,0.000303365,0.0000636772,0.0003127901,0.000007520124,0.000002289653,0.00002778472],"genre_scores_gemma":[0.9927466,0.00003957831,0.007161032,0.00003094902,0.000006124243,0.000005734608,6.65528e-7,0.000006873843,0.000002436816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4884556,"threshold_uncertainty_score":0.373072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08242702914330172,"score_gpt":0.3277783333304995,"score_spread":0.2453513041871978,"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."}}