{"id":"W2131582599","doi":"10.1002/we.1877","title":"Time‐adaptive wind turbine model for an LES framework","year":2015,"lang":"en","type":"article","venue":"Wind Energy","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; École Polytechnique Fédérale de Lausanne; National Supercomputing Center, Korea Institute of Science and Technology Information; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Wind gradient; Wind power; Geostrophic wind; Turbine; Wind direction; Planetary boundary layer; Wind profile power law; Wind shear; Thermal wind; Wind speed; Meteorology; Log wind profile; Environmental science; Marine engineering; Computer science; Geology; Engineering; Aerospace engineering; Turbulence; Physics; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001511897,0.0001961516,0.0001938917,0.00009761822,0.00007143791,0.00004014658,0.0002126798,0.0001886597,0.0000384674],"category_scores_gemma":[0.00004030019,0.0001825533,0.00005263666,0.0001350808,0.00004412811,0.0001868123,0.00004330611,0.0001402685,0.0000280182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001075476,"about_ca_system_score_gemma":0.0001001659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008331255,"about_ca_topic_score_gemma":0.00003798009,"domain_scores_codex":[0.9988642,0.00001818423,0.0001680828,0.000222982,0.000255975,0.0004705341],"domain_scores_gemma":[0.999184,0.0000448301,0.00001513536,0.0002502925,0.00009605559,0.0004097619],"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":[0.00006062618,0.00002953273,0.00000615417,0.000006937365,0.00005377472,0.000003726844,0.000590036,0.9566917,0.0002595444,0.002822457,0.03267297,0.006802505],"study_design_scores_gemma":[0.000414518,0.0001370863,0.00003098965,0.00002092352,0.000006566311,0.000003456904,0.00009831945,0.9466698,0.0008488679,0.01001251,0.0414725,0.0002844288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.221922,0.001504451,0.7184806,0.0003586733,0.001181092,0.0002866192,0.00007443596,0.001131926,0.05506025],"genre_scores_gemma":[0.961208,0.00003138565,0.03188242,0.0001642849,0.0007897443,0.00003636129,0.0000973813,0.00008557111,0.005704868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7392861,"threshold_uncertainty_score":0.7444307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04697559537868538,"score_gpt":0.2537373426362856,"score_spread":0.2067617472576002,"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."}}