{"id":"W4293086563","doi":"10.5194/hess-26-1545-2022","title":"Impact of correcting sub-daily climate model biases for hydrological studies","year":2022,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Environment and Climate Change Canada","funders":"Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Environmental science; Streamflow; Climate model; Climatology; Climate change; Precipitation; Diurnal cycle; Quantile; Scale (ratio); Temporal resolution; Downscaling; Hydrological modelling; Water cycle; Drainage basin; Meteorology; Econometrics; Geography; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004441954,0.0005552232,0.0004819441,0.0003767381,0.0004510726,0.0006822135,0.00071459,0.0005554773,0.0009289733],"category_scores_gemma":[0.01721445,0.0002643157,0.000612726,0.0007046534,0.0002860806,0.0008026391,0.0007628069,0.0007154727,0.0002318331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005681195,"about_ca_system_score_gemma":0.001574533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01343992,"about_ca_topic_score_gemma":0.01345683,"domain_scores_codex":[0.9984159,0.0009967932,0.0001026776,0.0001848823,0.0002087357,0.00009091693],"domain_scores_gemma":[0.9875247,0.007654789,0.0009110773,0.002134957,0.001492517,0.0002819147],"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":[0.001111306,0.0005217941,0.1959832,0.0002156831,0.0006774934,0.0003971517,0.0004528759,0.5658087,0.02414838,0.001669015,0.002282119,0.2067323],"study_design_scores_gemma":[0.0001402434,0.0002341521,0.07001409,0.0000422524,0.0001287961,0.0001079108,0.0001717745,0.9046357,0.02061316,0.0010055,0.002847808,0.00005858517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9400303,0.0002922749,0.05539405,0.0004801261,0.0001502585,0.00007476099,0.0005343547,0.001724412,0.00131937],"genre_scores_gemma":[0.9567572,0.00007670344,0.04215086,0.00009597489,0.00002171302,0.00003220974,0.0003879296,0.0001904674,0.0002870866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01343992,"threshold_uncertainty_score":0.02672338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06203263905684102,"score_gpt":0.3046750631829225,"score_spread":0.2426424241260815,"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."}}