{"id":"W2559271562","doi":"10.1002/joc.4924","title":"Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the <scp>CMIP5</scp> ensemble","year":2016,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Climate variability and models","field":"Environmental Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Downscaling; Climatology; Quantile; Environmental science; Climate extremes; Precipitation; Climate change; GCM transcription factors; Coupled model intercomparison project; Climate model; Meteorology; General Circulation Model; Statistics; Mathematics; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001544335,0.0001046336,0.0003574769,0.0001665641,0.00002478255,0.00001062069,0.0005498373,0.00007461405,0.00007300152],"category_scores_gemma":[0.001166124,0.00006398751,0.00007986888,0.0001220272,0.0001408345,0.0001165695,0.0002505342,0.0000960976,0.00000417619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009235837,"about_ca_system_score_gemma":0.00001804534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008072187,"about_ca_topic_score_gemma":0.0009735746,"domain_scores_codex":[0.9983028,0.0002270271,0.0007833077,0.0001856085,0.0002784479,0.0002227763],"domain_scores_gemma":[0.9969183,0.002135755,0.0006238198,0.0001589798,0.0001105954,0.00005260984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007786693,0.0006891374,0.4924081,0.000145735,0.0001968399,0.000007314576,0.0100276,0.003629086,0.3762486,0.01324643,0.0009661609,0.1016563],"study_design_scores_gemma":[0.01152318,0.002751379,0.3405817,0.001585374,0.0004627382,0.000606458,0.00871166,0.1580534,0.3618821,0.07174373,0.04152514,0.0005732382],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6238148,0.00002562628,0.3738961,0.001347533,0.0002297992,0.000245287,0.0001093222,0.000004584947,0.0003269003],"genre_scores_gemma":[0.9504035,0.00004673079,0.04938693,0.00006183072,0.00004126409,0.00003363243,0.000006262124,0.000009100469,0.00001072398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3265887,"threshold_uncertainty_score":0.2609335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04298216452501095,"score_gpt":0.3633097781720215,"score_spread":0.3203276136470105,"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."}}