{"id":"W3183684553","doi":"10.1016/j.jhydrol.2021.126646","title":"Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Lawrence Berkeley National Laboratory; Office of International Affairs; Office of Science; National Energy Research Scientific Computing Center; Canada Excellence Research Chairs, Government of Canada; U.S. Department of Energy","keywords":"Environmental science; Downscaling; Surface runoff; Watershed; Hydrology (agriculture); Hydrological modelling; Precipitation; Climate model; Catchment hydrology; Water balance; Climatology; Climate change; Water cycle; Water resources; Snowmelt; Snow; Meteorology; Geography; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006108811,0.0004066799,0.0004016602,0.0006604963,0.0003651584,0.0005936366,0.0009403147,0.0006616302,0.000997055],"category_scores_gemma":[0.001324776,0.0002942979,0.0009592299,0.001761288,0.0003272609,0.000893581,0.0004596817,0.0004754823,0.0001099294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001008332,"about_ca_system_score_gemma":0.001164807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04900276,"about_ca_topic_score_gemma":0.03087111,"domain_scores_codex":[0.999809,0.00005253365,0.00001678901,0.0000680395,0.00002907623,0.00002463969],"domain_scores_gemma":[0.9996305,0.0001126952,0.0000500434,0.00007674423,0.00009263186,0.00003748192],"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":[0.00003077185,0.00007868289,0.02519838,0.00003563546,0.0001208585,0.00009425465,0.00004059196,0.9638355,0.002043252,0.001549244,0.001067005,0.005905877],"study_design_scores_gemma":[0.00002740279,0.000008846313,0.01030513,0.000002920924,0.0000154717,0.000009679023,0.00002087189,0.987982,0.0003957854,0.0005780444,0.0006421292,0.00001174495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.953076,0.0001653761,0.03671402,0.0002494565,0.00003711622,0.00006609908,0.006127616,0.001239621,0.002324541],"genre_scores_gemma":[0.9767139,0.00008585962,0.01936956,0.00004209415,0.00001259103,0.00007439805,0.003392288,0.00006954125,0.0002398504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04900276,"threshold_uncertainty_score":0.09743506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009580702201591343,"score_gpt":0.2277130607350812,"score_spread":0.2181323585334898,"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."}}