{"id":"W4231853752","doi":"10.5194/wes-2017-7","title":"An engineering model for 3D turbulent wind inflow based on a limited set of random variables","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Deutscher Akademischer Austauschdienst; Pacific Institute for Climate Solutions","keywords":"Inflow; Stochastic process; Random variable; Turbine; Stochastic modelling; Covariance; Wind power; Wind speed; Random field; Computer science; Intermittency; Wind engineering; Turbulence; Mathematics; Mathematical optimization; Engineering; Statistics; Meteorology; Aerospace engineering; Structural engineering; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004309642,0.0003854616,0.0005390128,0.0003149939,0.00005784265,0.0001105893,0.0005171477,0.0003447944,0.00004077811],"category_scores_gemma":[0.0001238011,0.0003513662,0.0001531093,0.00004533127,0.00001839307,0.00008326911,0.0001065297,0.0003843055,0.000002767271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191045,"about_ca_system_score_gemma":0.0002364076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003296354,"about_ca_topic_score_gemma":0.000008962269,"domain_scores_codex":[0.998432,0.00001552913,0.0003772102,0.0003587875,0.0003550704,0.0004613357],"domain_scores_gemma":[0.9985336,0.0001448655,0.00006211996,0.0008996204,0.0001303613,0.0002294522],"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":[0.0002035047,0.00003574059,0.00001163029,0.0007142141,0.0001438979,0.000003733051,0.0001124257,0.9958802,0.0007817025,0.00007864358,0.0009088974,0.001125334],"study_design_scores_gemma":[0.001890958,0.00005628233,0.00006914293,0.0003408734,0.00002270546,5.836824e-7,0.000003260561,0.9917569,0.004155484,0.0001682137,0.001147891,0.0003876601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0264571,0.00008837623,0.9689303,0.0000860539,0.0003435024,0.000863112,0.0002111865,0.0004156053,0.002604756],"genre_scores_gemma":[0.8205785,0.00005510485,0.1778486,0.00003814748,0.0001935762,0.0002907582,0.000557058,0.0001107014,0.0003275314],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7941214,"threshold_uncertainty_score":0.9998938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02810833000657531,"score_gpt":0.2546489818956,"score_spread":0.2265406518890247,"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."}}