{"id":"W2893584644","doi":"10.1029/2018ms001444","title":"Explicitly Accounting for the Role of Remote Oceans in Regional Climate Modeling of South America","year":2018,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Climate variability and models","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université du Québec à Montréal; National Center for Atmospheric Research; National Science Foundation","keywords":"Downscaling; Teleconnection; Climate model; Climatology; Environmental science; Scale (ratio); General Circulation Model; Climate change; Domain (mathematical analysis); Atmospheric model; Meteorology; Computer science; Geography; Geology; El Niño Southern Oscillation; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006597702,0.0003482711,0.0003071249,0.0002678756,0.0004630277,0.0008107902,0.0007770346,0.0006100921,0.0008804045],"category_scores_gemma":[0.001734363,0.0002512155,0.000508018,0.0003850607,0.0004652559,0.0008241644,0.001182599,0.0007129194,0.00005606748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247532,"about_ca_system_score_gemma":0.001888005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.106706,"about_ca_topic_score_gemma":0.08403127,"domain_scores_codex":[0.9998281,0.00008645299,0.000008719686,0.00003814359,0.00001816527,0.00002048181],"domain_scores_gemma":[0.9995531,0.0001881411,0.00006785823,0.00007366812,0.00006847222,0.00004871553],"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":[0.00003902152,0.00003377166,0.03001867,0.00002120687,0.00005962545,0.00008431679,0.0001190716,0.9586967,0.00325356,0.003314567,0.0002619755,0.004097435],"study_design_scores_gemma":[0.00001916747,0.00001443519,0.005418566,0.000008111003,0.00001963983,0.000008580024,0.00004498502,0.9921528,0.0007134106,0.0009423557,0.0006480757,0.000009813022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774371,0.0001331473,0.01816164,0.0004160538,0.00001943064,0.00002778802,0.0001851401,0.0001234771,0.003496079],"genre_scores_gemma":[0.9951534,0.00004747481,0.004355846,0.00003793146,0.000006145476,0.00001672082,0.00005533202,0.0000192509,0.0003078902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.106706,"threshold_uncertainty_score":0.2121698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02734123891713549,"score_gpt":0.2744594884058315,"score_spread":0.247118249488696,"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."}}