{"id":"W2738669641","doi":"10.1002/2017jd026613","title":"Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes","year":2017,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Climate variability and models","field":"Environmental Science","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Oak Ridge National Laboratory; Natural Sciences and Engineering Research Council of Canada; University of Tsukuba; Ministry of Agriculture, Forestry and Fisheries; Microsoft Research; Canadian Foundation for Climate and Atmospheric Sciences; Natural Resources Canada; Université Laval; U.S. Department of Energy; National Science Foundation","keywords":"Forcing (mathematics); Climatology; Representative Concentration Pathways; Precipitation; Environmental science; GCM transcription factors; Climate model; General Circulation Model; Climate change; Meteorology; Atmospheric sciences; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002700987,0.00009980688,0.0003013995,0.00002010542,0.000267316,0.0001185838,0.0004138661,0.00009396744,0.00004893577],"category_scores_gemma":[0.008418445,0.00006641969,0.00002855478,0.0001551517,0.0003623094,0.0004984299,0.0007904983,0.0005241491,0.000001376538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001173389,"about_ca_system_score_gemma":0.00004138033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002037211,"about_ca_topic_score_gemma":0.001498053,"domain_scores_codex":[0.9979156,0.0007344186,0.0003408961,0.0002863651,0.0004610921,0.000261668],"domain_scores_gemma":[0.9976378,0.001468183,0.0001920794,0.0003896178,0.0001434521,0.0001688761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002328929,0.001147818,0.1584708,0.0001063136,0.00008889647,0.00002017129,0.001965862,0.002282951,0.6391432,0.001287535,0.001535216,0.1916223],"study_design_scores_gemma":[0.0006122767,0.0009251096,0.9363737,0.0001470303,0.00001946838,0.00000793908,0.000376137,0.04437738,0.0032432,0.0136393,0.000178109,0.0001003566],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982756,0.00004897188,0.0004351,0.0007314012,0.0000554667,0.0002881888,0.0000261967,0.000003315773,0.0001356946],"genre_scores_gemma":[0.9934794,0.0001478554,0.006270467,0.00001043334,0.00002974894,0.000008635707,0.000006489905,0.000004287999,0.00004270589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7779029,"threshold_uncertainty_score":0.9999341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.150681807602503,"score_gpt":0.4410751149611014,"score_spread":0.2903933073585985,"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."}}