{"id":"W1984414888","doi":"10.1175/jcli-d-11-00388.1","title":"Dynamical Downscaling over the Great Lakes Basin of North America Using the WRF Regional Climate Model: The Impact of the Great Lakes System on Regional Greenhouse Warming","year":2012,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Compute Canada","keywords":"Downscaling; Weather Research and Forecasting Model; Climatology; Environmental science; Climate model; Precipitation; Climate change; Snow; Coupled model intercomparison project; Greenhouse gas; Representative Concentration Pathways; Global warming; Water cycle; Geology; Meteorology; Geography; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003624223,0.0005669133,0.0003979336,0.0002903191,0.00039999,0.000598182,0.0006643834,0.0005559673,0.0009851613],"category_scores_gemma":[0.0009002854,0.000296436,0.0005681062,0.0004625736,0.0002634528,0.0005517579,0.000384676,0.0004053825,0.0001143376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009993431,"about_ca_system_score_gemma":0.001152294,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1214124,"about_ca_topic_score_gemma":0.1190963,"domain_scores_codex":[0.9999101,0.00003305615,0.000004410694,0.000024511,0.00001424936,0.00001357915],"domain_scores_gemma":[0.999848,0.00004696671,0.00002531167,0.00001601834,0.000044141,0.00001958741],"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":[0.00005703275,0.00005415812,0.01543397,0.00002310414,0.00007390251,0.000094796,0.00004925274,0.9760028,0.001440372,0.0004437082,0.0007899211,0.005536932],"study_design_scores_gemma":[0.00004067533,0.00003554016,0.009922446,0.000003498911,0.00002238363,0.00001094774,0.00003233779,0.9890109,0.0003124886,0.0002132068,0.0003834834,0.00001201877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991484,0.0001048567,0.00389676,0.0002360738,0.00002023376,0.00003771263,0.001241873,0.0002356882,0.002742845],"genre_scores_gemma":[0.9936783,0.0001071138,0.00459326,0.00002680952,0.00001123536,0.00004873081,0.0008786658,0.0000427253,0.0006131616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8785876,"threshold_uncertainty_score":0.2414114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03543204555247079,"score_gpt":0.2809113209385723,"score_spread":0.2454792753861015,"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."}}