{"id":"W2005972580","doi":"10.1115/imece2006-13330","title":"Development of a Draft Heavy Vehicle Rear Underride Guard Specification","year":2006,"lang":"en","type":"article","venue":"Design Engineering and Computers and Information in Engineering, Parts A and B","topic":"Transportation Safety and Impact Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Guard (computer science); Crash; Trailer; Crash test; Engineering; Motor vehicle crash; Aeronautics; Automotive engineering; Computer science; Poison control; Human factors and ergonomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001210276,0.000138302,0.0001716825,0.0002446428,0.00003009755,0.00004658035,0.00003312409,0.00005489942,0.000002188907],"category_scores_gemma":[0.000003324399,0.000144872,0.00001815299,0.0001614998,0.00001225951,0.0005307772,0.000006921576,0.00007580262,8.623599e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001950445,"about_ca_system_score_gemma":0.000007887071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001360479,"about_ca_topic_score_gemma":0.000002287763,"domain_scores_codex":[0.9992942,0.000003442556,0.0003928563,0.00007523467,0.00008821053,0.000146116],"domain_scores_gemma":[0.9997954,0.00003037289,0.00003513818,0.00006417506,0.00001899376,0.00005588177],"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":[0.000007135548,0.00000709222,0.0002988839,0.0002107408,0.00002169839,5.885264e-7,0.001326193,0.9905368,0.001180946,0.001610013,0.00006307479,0.004736806],"study_design_scores_gemma":[0.0003489351,0.00001810118,0.0555957,0.00008303089,0.00000939872,0.000005013824,0.00005406096,0.9341784,0.001474269,0.00001177274,0.008035004,0.000186311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3327239,0.0002432075,0.6667266,0.00001791256,0.00005812119,0.00008207659,0.000002817063,0.00009680584,0.00004850437],"genre_scores_gemma":[0.9772682,0.0001968346,0.02245899,0.000009374026,0.0000194426,0.00001046407,0.00002397696,0.000009465061,0.000003231454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6445444,"threshold_uncertainty_score":0.590771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006972089986398537,"score_gpt":0.1664248413236708,"score_spread":0.1594527513372723,"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."}}