{"id":"W4291624854","doi":"10.1175/jhm-d-21-0142.1","title":"En-GARD: A Statistical Downscaling Framework to Produce and Test Large Ensembles of Climate Projections","year":2022,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Climate variability and models","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canmore Museum and Geoscience Centre; University of Saskatchewan","funders":"U.S. Army Corps of Engineers; Division of Atmospheric and Geospace Sciences; Earth Sciences Division; Nuclear Safety and Security Commission; National Aeronautics and Space Administration; National Institute of Advanced Industrial Science and Technology; National Center for Atmospheric Research; Bureau of Reclamation; National Science Foundation","keywords":"Downscaling; Computer science; Climate model; Climate change; Scale (ratio); Climatology; Environmental science; Statistical model; Meteorology; Machine learning; Precipitation; Geography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001704721,0.00009096674,0.0002874026,0.0001217801,0.0001832754,0.000009285461,0.000167514,0.00005446434,0.001571356],"category_scores_gemma":[0.001209954,0.00008076076,0.00004983892,0.0002592247,0.0001019543,0.00007931429,0.000420195,0.0003916397,0.000009224136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009285367,"about_ca_system_score_gemma":0.00002257686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004038347,"about_ca_topic_score_gemma":0.00002912281,"domain_scores_codex":[0.9985579,0.0002338526,0.000457411,0.0002014922,0.0002658206,0.0002835388],"domain_scores_gemma":[0.9985511,0.0009597171,0.0002079957,0.000152902,0.00001603653,0.0001122947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001210956,0.004211037,0.714512,0.000198257,0.0001890694,0.0002484315,0.01365701,0.04907165,0.1967782,0.01081444,0.002728099,0.0063809],"study_design_scores_gemma":[0.005818894,0.03215967,0.6616524,0.000188837,0.0009490002,0.01143947,0.005129128,0.02867157,0.005527758,0.1188758,0.1277722,0.001815258],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945582,0.00003533526,0.003419305,0.001218061,0.0001957376,0.0001638013,0.0001247072,0.000007121508,0.0002777458],"genre_scores_gemma":[0.9830661,0.00005685979,0.01653571,0.000264682,0.00003585513,0.00001208935,0.000001742846,0.000009050247,0.00001792785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1912504,"threshold_uncertainty_score":0.9993414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008147860010429954,"score_gpt":0.2619747180622194,"score_spread":0.2538268580517895,"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."}}