{"id":"W2052451096","doi":"10.4271/2013-01-1189","title":"Virtual Road Load Data Acquisition using Full Vehicle Simulations","year":2013,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Computer science; Vehicle dynamics; Data modeling; Data acquisition; Automotive engineering; Engineering; Database; Operating system","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004580482,0.0007089689,0.0008107373,0.0001707893,0.0003936321,0.0002851364,0.001434043,0.0007754394,0.0009214511],"category_scores_gemma":[0.0002561233,0.0006737652,0.0002627008,0.0006604264,0.0003728943,0.001286535,0.000556901,0.0009643422,0.0003855115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005567984,"about_ca_system_score_gemma":0.00008754999,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002332099,"about_ca_topic_score_gemma":0.0357095,"domain_scores_codex":[0.9958096,0.000115783,0.001123851,0.001044274,0.0009329111,0.0009735788],"domain_scores_gemma":[0.9966182,0.0002610207,0.000151728,0.002366334,0.0002044824,0.0003982468],"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":[0.00006016619,0.0001386778,0.00008104955,0.00003116652,0.00006987893,0.00001320583,0.00001410187,0.008355029,0.9787594,0.002964641,0.001473196,0.008039468],"study_design_scores_gemma":[0.00123563,0.0006299418,0.9820373,0.0002064865,0.000125375,0.00007131853,0.000116384,0.003389514,0.00003832451,0.001118021,0.00987544,0.001156269],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759341,0.0004587426,0.00003703605,0.001045901,0.0004915906,0.001363266,0.0003245029,0.003660289,0.01668455],"genre_scores_gemma":[0.9972234,0.00007417455,0.001040298,0.0006417212,0.0003471486,0.0001524325,0.0001970916,0.0001782938,0.0001454238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9819562,"threshold_uncertainty_score":0.9999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430620324415173,"score_gpt":0.2371048472455826,"score_spread":0.2227986440014309,"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."}}