{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006367918,0.0005924809,0.0008163261,0.0005201056,0.0003241239,0.0007898231,0.0006122523,0.0009162845,0.002285408],"category_scores_gemma":[0.001064034,0.0004017309,0.0006566003,0.0002650486,0.0003093784,0.0005209818,0.0004983316,0.0005319359,0.0003717753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004023878,"about_ca_system_score_gemma":0.0007886799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00152437,"about_ca_topic_score_gemma":0.001350076,"domain_scores_codex":[0.9996809,0.00009289371,0.00001474034,0.00004611255,0.0001231609,0.00004230283],"domain_scores_gemma":[0.9996337,0.000154429,0.00006803626,0.0000293492,0.00009673841,0.00001771681],"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":[0.00006401096,0.00002979362,0.0002200496,0.00007612379,0.00001119572,0.00005377412,0.00002655106,0.9768172,0.0101883,0.001342422,0.0001682822,0.01100226],"study_design_scores_gemma":[0.000004593959,0.00006328025,0.00009447498,0.000004388836,0.000004667166,0.00000819221,0.000006203071,0.9983222,0.001046733,0.0001777128,0.0002645125,0.000002949497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1098584,0.0003238503,0.8788013,0.0001587446,0.00003289632,0.0001403016,0.00008486011,0.0004725472,0.0101271],"genre_scores_gemma":[0.8831269,0.0002172138,0.1118352,0.00002747266,0.000007686158,0.0002851831,0.0001074537,0.00006177671,0.004331136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002285408,"threshold_uncertainty_score":0.007645488,"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."}}