{"id":"W4403755532","doi":"10.2172/2473210","title":"WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset","year":2024,"lang":"en","type":"report","venue":"","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Renewable Energy Laboratory; Argonne National Laboratory; Office of Energy Efficiency; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy; Wind Energy Technologies Office; National Science Foundation","keywords":"Term (time); Computer science; Meteorology; Environmental science; Geography; Physics; Astronomy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000147838,0.0002114952,0.0001880916,0.00005589602,0.00004275033,0.0001658938,0.0002065965,0.0001406825,0.001119424],"category_scores_gemma":[0.000009907147,0.000145341,0.00008130054,0.00009775958,0.00002313945,0.0001576152,0.0000757478,0.0003331836,0.0007522916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002315256,"about_ca_system_score_gemma":0.0001133749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000898232,"about_ca_topic_score_gemma":0.00009815289,"domain_scores_codex":[0.9990324,0.000007519369,0.0003327975,0.0002013613,0.0002403811,0.0001855636],"domain_scores_gemma":[0.9994417,0.00003248044,0.00003826014,0.0004108791,0.00003298015,0.00004367172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001373799,0.00001091424,0.001264135,0.0002768452,0.0002546874,0.00001170917,0.00002175387,0.002471817,0.0001503045,0.0003734082,0.943372,0.05179102],"study_design_scores_gemma":[0.0001081111,0.00002237437,0.0151921,0.0001456218,0.0002092975,0.00002267112,0.00003214275,0.002750136,0.0004596322,0.00007084046,0.9805872,0.0003999046],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3021584,0.00451926,0.01988842,0.001451065,0.01625539,0.002111946,0.01422763,0.001595989,0.6377919],"genre_scores_gemma":[0.8261434,0.002526375,0.002955387,0.0004026318,0.00373259,0.0001709682,0.1089837,0.0004408867,0.05464402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5831479,"threshold_uncertainty_score":0.9997937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03495570594029042,"score_gpt":0.2802633006479258,"score_spread":0.2453075947076354,"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."}}