{"id":"W4407303051","doi":"10.1016/j.ejrh.2025.102223","title":"Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology Regional Studies","topic":"Climate variability and models","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Climatology; Precipitation; Environmental science; Econometrics; Geography; Meteorology; Mathematics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001783241,0.0001110043,0.0003275438,0.0002221345,0.0003485832,0.000007750361,0.00008578021,0.00006340347,0.000008027849],"category_scores_gemma":[0.0001879026,0.00008295519,0.00007169479,0.0002944805,0.0002299428,0.0002160178,0.00004489873,0.0001589892,1.946968e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005878219,"about_ca_system_score_gemma":0.0002184026,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03461669,"about_ca_topic_score_gemma":0.163642,"domain_scores_codex":[0.9986579,0.0003713474,0.0004584155,0.0001902503,0.0001545147,0.0001676099],"domain_scores_gemma":[0.9985731,0.0006928692,0.0003635626,0.0001156632,0.0002029748,0.00005186824],"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":[0.0006740454,0.00125608,0.1050787,0.00006158885,0.001426384,0.00009040844,0.008241262,0.8706302,0.0003405999,0.002568148,0.00531048,0.004322029],"study_design_scores_gemma":[0.006982019,0.01377755,0.1534723,0.0002590049,0.002341406,0.004836175,0.04891593,0.6796654,0.0001063628,0.08525445,0.003800062,0.000589359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9324867,0.0001647648,0.0643198,0.001563551,0.000443148,0.0006130199,0.000004873581,0.000006349425,0.0003978008],"genre_scores_gemma":[0.9744009,0.00008529147,0.02516352,0.0001044706,0.00002682312,0.00008465909,0.000001823341,0.000005265708,0.0001272939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1909648,"threshold_uncertainty_score":0.9718119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1177967016318476,"score_gpt":0.4040377917311498,"score_spread":0.2862410900993022,"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."}}