{"id":"W2789587597","doi":"10.1016/j.jhydrol.2018.03.040","title":"Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling","year":2018,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":109,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"National Science Foundation; National Aerospace Science Foundation of China; Natural Sciences and Engineering Research Council of Canada; Wuhan University; Hydro-Québec; National Natural Science Foundation of China","keywords":"Precipitation; Environmental science; Snow; Climate model; Variable (mathematics); Climatology; Watershed; Metric (unit); Climate change; Arid; Meteorology; Mathematics; Computer science; Geology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001266683,0.0001135966,0.0003166323,0.0000790844,0.0001390284,0.00000367711,0.0002712527,0.0001075522,0.0001310766],"category_scores_gemma":[0.0001856607,0.00006886577,0.00008763198,0.0001162797,0.000404205,0.0001321319,0.0002382663,0.000273121,0.0000257419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000390901,"about_ca_system_score_gemma":0.000008051416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004744713,"about_ca_topic_score_gemma":0.00002699951,"domain_scores_codex":[0.998709,0.0001377245,0.0005700081,0.0001355355,0.0001827554,0.0002649482],"domain_scores_gemma":[0.9990189,0.0001530736,0.000595281,0.0001576181,0.00003660727,0.00003852209],"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":[0.0005911216,0.0001149816,0.04509108,0.000007530801,0.00007457867,0.000004844184,0.00109765,0.9490992,0.002468744,0.0004266875,0.000299235,0.0007242847],"study_design_scores_gemma":[0.0004808317,0.001608254,0.001249125,0.00002708623,0.00007901698,0.00006734085,0.00007883705,0.9892915,0.0009468991,0.006033912,0.00006782264,0.00006932033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547153,0.00002876649,0.03801913,0.0007115112,0.0003367996,0.00007821561,0.000001241628,0.000005830787,0.006103205],"genre_scores_gemma":[0.9985884,0.00004735969,0.0007228254,0.0005431892,0.00006211612,0.000001125947,4.81356e-7,0.000006546221,0.00002796356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04387309,"threshold_uncertainty_score":0.2808265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01872770102329722,"score_gpt":0.253864391215387,"score_spread":0.2351366901920897,"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."}}