{"id":"W4379365342","doi":"10.36001/ijphm.2023.v14i3.3128","title":"Ground Fault Diagnostics for Automotive Electronic Control Units","year":2023,"lang":"en","type":"article","venue":"International Journal of Prognostics and Health Management","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"CAN bus; Offset (computer science); Ground; Voltage; Frame (networking); Automotive industry; Computer science; Electronic control unit; Engineering; Real-time computing; Embedded system; Automotive engineering; Electrical engineering; Computer hardware; Computer network","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":[0.0006123732,0.00009805983,0.0001839249,0.00024205,0.00005269801,0.00009199847,0.0001484565,0.0000296341,0.000006028025],"category_scores_gemma":[0.00009195994,0.00009053032,0.00004532898,0.0001336533,0.00001218458,0.00009228113,0.00001918071,0.0001027764,0.000007449211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001467249,"about_ca_system_score_gemma":0.0000502582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006713861,"about_ca_topic_score_gemma":0.000007958513,"domain_scores_codex":[0.9988942,0.00002048033,0.0004637512,0.00008011339,0.0002997915,0.0002416664],"domain_scores_gemma":[0.9989564,0.0002318918,0.0001695772,0.00005023253,0.0004961382,0.00009569227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002976318,0.0002142046,0.004941427,0.001169702,0.004350838,0.0001614083,0.001507298,0.3407047,0.00004655835,0.2235928,0.07937443,0.343639],"study_design_scores_gemma":[0.007078077,0.0006260592,0.07959846,0.0004162792,0.0001229911,0.00003773315,0.0007087711,0.6286771,0.00001037612,0.00801073,0.274432,0.0002814265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321572,0.01148719,0.7897534,0.03896247,0.01433158,0.0065606,0.0003849727,0.0007844146,0.00557819],"genre_scores_gemma":[0.9946025,0.004166978,0.0002466267,0.0004282946,0.0003541045,0.00002831639,0.00002096924,0.00001869531,0.0001335013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8624453,"threshold_uncertainty_score":0.3691719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02553464929730045,"score_gpt":0.3042137731713274,"score_spread":0.2786791238740269,"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."}}