{"id":"W2036550307","doi":"10.4271/2015-01-1362","title":"Lightweight Optimal Design of a Rear Bumper System Based on Surrogate Models","year":2015,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Vehicle Noise and Vibration Control","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Surrogate model; Computer science; Automotive engineering; Engineering; Machine learning","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"],"consensus_categories":[],"category_scores_codex":[0.0009805987,0.0007859687,0.001129527,0.000312907,0.0001598321,0.00009175068,0.0008541088,0.0008620368,0.0001124934],"category_scores_gemma":[0.0002723106,0.0006698428,0.0004119269,0.0006838702,0.0003642701,0.0005831369,0.0001243943,0.0009338948,0.0001555126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004055683,"about_ca_system_score_gemma":0.0001690223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001829563,"about_ca_topic_score_gemma":0.001295506,"domain_scores_codex":[0.995719,0.0002509993,0.001196663,0.0008415724,0.001146605,0.0008451968],"domain_scores_gemma":[0.9970862,0.0005213196,0.000174836,0.001423783,0.0002169546,0.0005769689],"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":[0.0008344484,0.0002408574,0.00001367594,0.0001201487,0.00006639103,0.00005820384,0.00003590803,0.1410545,0.8408884,0.01217758,0.003843119,0.0006666895],"study_design_scores_gemma":[0.04063252,0.03873317,0.6918712,0.009514699,0.002272626,0.0007325916,0.001888269,0.03492198,0.04274961,0.008799835,0.1116946,0.01618891],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5639549,0.002564723,0.004600432,0.008382165,0.002402265,0.01045186,0.0006855341,0.0352548,0.3717033],"genre_scores_gemma":[0.9896793,0.0000672854,0.008798311,0.0005611859,0.0001676381,0.0003716361,0.00003078028,0.0001887388,0.0001351707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7981389,"threshold_uncertainty_score":0.9995753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0222588285445325,"score_gpt":0.2234144839468265,"score_spread":0.201155655402294,"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."}}