{"id":"W1968405005","doi":"10.1007/s12650-014-0263-9","title":"Flow visualization of light vehicle–trailer systems aerodynamics","year":2014,"lang":"en","type":"article","venue":"Journal of Visualization","topic":"Aerodynamics and Fluid Dynamics Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Aerodynamics; Trailer; Wind tunnel; Towing; Flow visualization; Drag; Aerospace engineering; Marine engineering; Visualization; Reynolds number; Computer science; Truck; Simulation; Flow (mathematics); Engineering; Automotive engineering; Meteorology; Mechanics; Physics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002508644,0.0003338283,0.000281583,0.0007558607,0.0005110594,0.001119253,0.0002269763,0.0003911311,0.007524591],"category_scores_gemma":[0.0006808342,0.0001626756,0.0002024212,0.0003275113,0.0002707131,0.0007027127,0.0006283008,0.0005536334,0.00035856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003338158,"about_ca_system_score_gemma":0.0004405391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002518833,"about_ca_topic_score_gemma":0.001455808,"domain_scores_codex":[0.9998918,0.00002436891,0.000004336133,0.00001632979,0.00003804477,0.00002519414],"domain_scores_gemma":[0.999752,0.00009128088,0.00002125004,0.00002348884,0.0000665202,0.00004553638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001500857,0.0004044514,0.01564861,0.0003265968,0.00008928991,0.0009216809,0.00173638,0.1538007,0.4932404,0.03334013,0.01378699,0.285204],"study_design_scores_gemma":[0.00008557052,0.0001733303,0.01638131,0.0000445765,0.00002426867,0.0002983408,0.0002980269,0.9156823,0.04626544,0.008154485,0.01253247,0.00005976247],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7672504,0.0007347519,0.2100816,0.0007409593,0.0002754128,0.0001127918,0.0004723427,0.00305059,0.01728108],"genre_scores_gemma":[0.9714173,0.000170158,0.02473666,0.00004784262,0.00006679961,0.00001387845,0.0001555859,0.0001945546,0.003197235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007524591,"threshold_uncertainty_score":0.02517223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008523037986399565,"score_gpt":0.2635712438634209,"score_spread":0.2550482058770213,"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."}}