{"id":"W4232022529","doi":"10.5194/wes-2018-9","title":"Near wake analysis of actuator line method immersed in turbulent flow using large-eddy simulations","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wake; Turbulence; Detached eddy simulation; Mechanics; Wind tunnel; Physics; Wind power; Rotor (electric); Turbulence kinetic energy; Large eddy simulation; Marine engineering; Meteorology; Reynolds-averaged Navier–Stokes equations; Engineering; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005200335,0.000282556,0.0006816088,0.0009427593,0.00004692236,0.00005338097,0.0002476259,0.0002869301,0.001411192],"category_scores_gemma":[0.00007101534,0.0002639589,0.0002481861,0.001066472,0.000026713,0.00005625529,0.0002888432,0.0003887413,0.00001107112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002563602,"about_ca_system_score_gemma":0.0002061895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004191036,"about_ca_topic_score_gemma":0.0006220581,"domain_scores_codex":[0.9981195,0.00007924093,0.0006003815,0.0003568045,0.0003885501,0.0004555586],"domain_scores_gemma":[0.9989737,0.0001217249,0.00006486198,0.000521873,0.0001601805,0.0001576661],"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":[0.00001165127,0.00005444998,0.0008106688,0.00006875686,0.001834403,0.000006232481,0.0004171095,0.9955062,0.0004932176,0.00001101827,0.0001222154,0.0006640421],"study_design_scores_gemma":[0.000286384,0.00000982802,0.002990808,0.00005040581,0.0002904973,3.419816e-7,0.00004356248,0.9928086,0.002512877,0.00006731179,0.0006766396,0.0002627345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4787851,0.0001012204,0.5199358,0.00003896149,0.0001777251,0.0002043587,0.000115811,0.00009426804,0.0005467549],"genre_scores_gemma":[0.621212,0.00004101256,0.3780622,0.00002291963,0.00007864291,0.00001386296,0.0004083568,0.00003915582,0.000121867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1424269,"threshold_uncertainty_score":0.9999813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03737690741948658,"score_gpt":0.3351183872506995,"score_spread":0.297741479831213,"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."}}