{"id":"W2885505824","doi":"10.1002/hyp.13251","title":"Using reanalysis‐driven regional climate model outputs for hydrology modelling","year":2018,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Environmental science; Climate model; Climate change; Climatology; Scale (ratio); Hydrological modelling; Meteorology; Geology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001597531,0.000698431,0.0003366195,0.0006629805,0.0002971944,0.001116697,0.0007306958,0.0004248656,0.001388345],"category_scores_gemma":[0.00390987,0.000222978,0.0007094956,0.001425196,0.0002137008,0.0006363388,0.0003154501,0.00045027,0.0003934201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002847378,"about_ca_system_score_gemma":0.003481538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4361993,"about_ca_topic_score_gemma":0.3745969,"domain_scores_codex":[0.9994839,0.0002132931,0.00003714466,0.00008636004,0.0001349271,0.00004434912],"domain_scores_gemma":[0.9985523,0.0003890209,0.0001359745,0.0002075152,0.0006688351,0.00004641228],"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":[0.00009735744,0.00009770381,0.05247004,0.000143595,0.0004017015,0.00009178302,0.00008890427,0.9112559,0.002656705,0.001622524,0.002485887,0.0285879],"study_design_scores_gemma":[0.00006767391,0.00003137812,0.04039245,0.00003308171,0.00007968766,0.00001413731,0.0000610616,0.9506255,0.003578227,0.0008188512,0.004249038,0.00004881617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8573465,0.0009127314,0.1022075,0.0007324511,0.0001841228,0.0003515311,0.02031793,0.003439836,0.01450733],"genre_scores_gemma":[0.9706457,0.0002924408,0.02378895,0.00004677982,0.00001675903,0.000111925,0.00371492,0.0001420808,0.001240448],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4361993,"threshold_uncertainty_score":0.8673208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1034077952670016,"score_gpt":0.2982549896267667,"score_spread":0.1948471943597651,"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."}}