{"id":"W2954862674","doi":"10.1073/pnas.1903680116","title":"Wind farm power optimization through wake steering","year":2019,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":420,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Wind power; Wake; Renewable energy; Meteorology; Production (economics); Environmental science; Power (physics); Wind speed; Marine engineering; Engineering; Geography; Electrical engineering; Physics; Economics; Aerospace engineering; Microeconomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002745782,0.0004924492,0.0003658544,0.0002592074,0.0002171128,0.0004065293,0.000283285,0.0002835455,0.001155313],"category_scores_gemma":[0.0006456517,0.0002537409,0.0002391066,0.0002069173,0.0002923849,0.0003063643,0.0003986075,0.0003711221,0.0002202949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668618,"about_ca_system_score_gemma":0.0005269801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002901963,"about_ca_topic_score_gemma":0.004069471,"domain_scores_codex":[0.9999106,0.0000213222,0.00000439828,0.0000161788,0.00002806435,0.00001942064],"domain_scores_gemma":[0.9998114,0.00007270349,0.00002968067,0.00001257564,0.00005631027,0.00001748942],"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":[0.00005604612,0.00004681047,0.0004192802,0.00001977447,0.0000094165,0.00003522451,0.00002673126,0.9784039,0.005058658,0.001280927,0.0004703648,0.01417278],"study_design_scores_gemma":[0.000005839832,0.00002347145,0.00007257479,9.968969e-7,0.000001524379,0.000002298302,0.000005040466,0.9990596,0.0003641417,0.0003128757,0.000150095,0.000001672313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2833848,0.0002723694,0.7015903,0.0002311822,0.00007319454,0.0001152458,0.00007845038,0.0006920472,0.01356248],"genre_scores_gemma":[0.9659452,0.00004044624,0.03170015,0.00003953983,0.000007536024,0.0000477996,0.00005115585,0.00004648004,0.00212173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002901963,"threshold_uncertainty_score":0.005770147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435935709963639,"score_gpt":0.264259839587988,"score_spread":0.2399004824883516,"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."}}