{"id":"W2292070790","doi":"10.4271/2016-01-0378","title":"Fatigue Life Prediction of an Automotive Chassis System with Combined Hardening Material Model","year":2016,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Mechanical Engineering and Vibrations Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Chassis; Automotive industry; Hardening (computing); Automotive engineering; Computer science; Materials science; Mechanical engineering; Engineering; Composite material","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":[0.0005095227,0.0005853231,0.000835681,0.000271568,0.0001948189,0.00007868966,0.0007006452,0.0006420154,0.0001873953],"category_scores_gemma":[0.0004239704,0.0004064501,0.0001976352,0.0005486875,0.0003921104,0.0005894459,0.0001614376,0.0006173025,0.0000248954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003326511,"about_ca_system_score_gemma":0.00009989449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001537208,"about_ca_topic_score_gemma":0.001545576,"domain_scores_codex":[0.9965296,0.0001127519,0.0009445636,0.000737289,0.0009296318,0.0007461939],"domain_scores_gemma":[0.9978209,0.0002359046,0.0001163138,0.001103697,0.0001881869,0.0005350013],"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":[0.0004111405,0.0001509488,0.0000109415,0.0001898579,0.00008529866,0.00001285628,0.00002087232,0.01178967,0.9756905,0.01002438,0.0003371517,0.001276347],"study_design_scores_gemma":[0.006597262,0.01162532,0.9406098,0.004299943,0.0003408205,0.0001899341,0.0003876948,0.005238092,0.02558378,0.0008891589,0.001544718,0.002693486],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677595,0.00007124,0.003712656,0.001148169,0.0004896275,0.001713913,0.0008527952,0.01151434,0.01273779],"genre_scores_gemma":[0.9927032,0.00005641493,0.006308964,0.0000519177,0.0001287742,0.0004069501,0.00005745242,0.0001634685,0.000122878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9501067,"threshold_uncertainty_score":0.9998387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01815433166612772,"score_gpt":0.2322419831394893,"score_spread":0.2140876514733616,"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."}}