{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001721516,0.0004009886,0.0002238616,0.0006677452,0.0001689521,0.0003999321,0.0003886503,0.0003629139,0.001494684],"category_scores_gemma":[0.0009250516,0.00009760905,0.000154566,0.0003025009,0.0001602649,0.0005027398,0.0002753311,0.0002029167,0.0002063038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005185475,"about_ca_system_score_gemma":0.0004483923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002293139,"about_ca_topic_score_gemma":0.002292767,"domain_scores_codex":[0.9998024,0.00006207078,0.000008526788,0.00003166643,0.00007600453,0.00001920504],"domain_scores_gemma":[0.9997283,0.000114556,0.00005320657,0.00002824026,0.00006553614,0.00001021149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005250068,0.0001421983,0.01303005,0.0003557198,0.00005338996,0.0006700987,0.0002408827,0.5577198,0.08024628,0.008274137,0.002667922,0.3360744],"study_design_scores_gemma":[0.000009791346,0.0001296056,0.002256884,0.00001601676,0.00001135982,0.0001059656,0.00004778058,0.9846705,0.01025496,0.001574998,0.0009149625,0.000007163099],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2783169,0.000936734,0.7136571,0.0002242271,0.00006933697,0.00009694798,0.0001553059,0.002879388,0.003664059],"genre_scores_gemma":[0.9882025,0.0001023623,0.0108788,0.00001460342,0.000006489518,0.00001413905,0.00005050277,0.00001688021,0.0007137344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002293139,"threshold_uncertainty_score":0.005000174,"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."}}