{"id":"W3175181246","doi":"10.1016/j.jweia.2021.104698","title":"On the correlation between aerodynamic drag and wake flow for a generic high-speed train","year":2021,"lang":"en","type":"article","venue":"Journal of Wind Engineering and Industrial Aerodynamics","topic":"Aerodynamics and Fluid Dynamics Research","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Science and Technology of the People's Republic of China; Central South University","keywords":"Wake; Aerodynamics; Drag; Drag coefficient; Flow (mathematics); Aerodynamic force; Mechanics; Aerodynamic drag; Flow visualization; Physics; Simulation; Computer science","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":[0.000777909,0.0002848574,0.0004590975,0.0001963215,0.0001372427,0.0001620994,0.0001535624,0.000308663,0.000007856722],"category_scores_gemma":[0.0004205972,0.0002307347,0.0001285611,0.0003389215,0.00006092391,0.0001551036,0.00004842671,0.0009099481,8.917564e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630257,"about_ca_system_score_gemma":0.0000830384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004776516,"about_ca_topic_score_gemma":0.0000138423,"domain_scores_codex":[0.9984542,0.00004912684,0.0005863602,0.0002049982,0.0003236794,0.0003816395],"domain_scores_gemma":[0.9986529,0.0006687758,0.0001097093,0.0002004268,0.0001728482,0.0001953808],"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":[0.00004242315,0.00002017806,0.000294309,0.00005538783,0.0002379766,0.000023076,0.0001165299,0.9787437,0.008075314,0.004223033,0.0002918567,0.007876172],"study_design_scores_gemma":[0.001313523,0.0002121119,0.006721339,0.0001242128,0.00007727532,0.00008263106,0.00005750799,0.9902875,0.00004116286,0.0004994527,0.0003308472,0.0002524187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668172,0.0004167334,0.03085031,0.0005710926,0.0009245774,0.0002285895,0.0001158673,0.00003987024,0.0000357291],"genre_scores_gemma":[0.9964097,0.0004965998,0.002126476,0.00002314601,0.0007307299,0.000003683327,0.00005203511,0.00007428031,0.00008331284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02959251,"threshold_uncertainty_score":0.9409087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02413302827337944,"score_gpt":0.2157649118699138,"score_spread":0.1916318835965344,"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."}}