{"id":"W2026044798","doi":"10.4271/2015-01-0528","title":"Fracture Characterization of Automotive Alloys in Shear Loading","year":2015,"lang":"en","type":"article","venue":"SAE International Journal of Materials and Manufacturing","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Materials science; Automotive industry; Shear (geology); Characterization (materials science); Composite material; Fracture (geology); Structural engineering; Forensic engineering; Engineering; Nanotechnology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003183439,0.00008324639,0.0001881244,0.0002348973,0.000006263147,0.0000403262,0.0001173914,0.00005279159,0.00004615088],"category_scores_gemma":[0.00004163701,0.00007391029,0.0000246909,0.00001876733,0.00001274206,0.0003745706,0.00002703292,0.00007524226,0.000001204116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006461573,"about_ca_system_score_gemma":0.00001190535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001615547,"about_ca_topic_score_gemma":0.000001371348,"domain_scores_codex":[0.9992036,0.00002133096,0.0004377215,0.00005166244,0.000212899,0.00007280962],"domain_scores_gemma":[0.9995821,0.00001951984,0.0001918709,0.00004093131,0.0001235452,0.00004209916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001153974,0.00003246307,0.0004740939,0.00008582338,0.0001181415,0.00003977754,0.001689029,0.01069592,0.9819105,0.0002395603,0.0001001468,0.004499149],"study_design_scores_gemma":[0.0003460092,0.00003058128,0.02063505,0.0001543288,0.000005726384,0.00003533264,0.00004050318,0.0001094695,0.9755771,0.001417625,0.001584087,0.00006419649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973535,0.00003415646,0.001524694,0.00009609233,0.0007869875,0.00005210565,0.00001057311,0.00002495184,0.0001169232],"genre_scores_gemma":[0.9989038,0.00007573306,0.0007890958,0.00004068264,0.000156451,9.082005e-7,0.000008689639,0.00001261947,0.00001198827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02016095,"threshold_uncertainty_score":0.3013974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298109447516501,"score_gpt":0.2484713181588535,"score_spread":0.2354902236836884,"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."}}