{"id":"W2768928791","doi":"10.5194/gmd-2017-272","title":"Comparison of dealiasing schemes in large-eddy simulation of neutrally-stratified atmospheric boundary-layer type flows","year":2017,"lang":"en","type":"article","venue":"","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Center for High Performance Computing; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; University of Utah","keywords":"Fourier series; Truncation error; Large eddy simulation; Fourier transform; Smoothing; Mathematics; Turbulence modeling; Truncation (statistics); Fourier analysis; Discrete Fourier series; Mathematical analysis; Turbulence; Applied mathematics; Physics; Mechanics; Short-time Fourier transform; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001382445,0.000088856,0.0002203362,0.000004994869,0.000167816,0.00002426029,0.0001736244,0.00004459711,0.0005486645],"category_scores_gemma":[0.00004859465,0.00007457128,0.00003599188,0.00007821636,0.000122996,0.0002143474,0.0001203288,0.00006265786,0.00002545583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002517218,"about_ca_system_score_gemma":0.000009594316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003763123,"about_ca_topic_score_gemma":0.001311988,"domain_scores_codex":[0.9992244,0.00001878365,0.0002732859,0.0001508332,0.0001704953,0.0001622519],"domain_scores_gemma":[0.9995341,0.00003252349,0.000159099,0.0002357242,0.00001240226,0.00002618693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002778436,0.0001502974,0.9056579,0.0000160429,0.000009140237,7.633416e-7,0.0005997593,0.0746903,0.01507859,0.00007572212,0.0001552968,0.003538367],"study_design_scores_gemma":[0.0004214381,0.00007538816,0.7818159,0.00002801944,0.000008768973,1.170397e-7,0.0003225059,0.2033084,0.01193944,0.0001249706,0.001839142,0.000115805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990577,0.00004951899,0.0009557849,0.00007212049,0.00008617922,0.0001003332,0.000001732076,0.00000986626,0.008147472],"genre_scores_gemma":[0.9943938,0.000005019724,0.005250439,0.00001818285,0.00001526775,0.000001195925,0.000001791068,0.000005714397,0.0003086311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1286181,"threshold_uncertainty_score":0.6007491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0410790330913637,"score_gpt":0.3358769024614957,"score_spread":0.294797869370132,"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."}}