{"id":"W4388115974","doi":"10.36001/phmconf.2023.v15i1.3520","title":"Automotive Electronic Control Unit Ground Line Health Monitoring Method","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Automotive engineering; Automotive industry; CAN bus; Electronic control unit; Engineering; Fault (geology); Reliability (semiconductor); Controller (irrigation); Embedded system; Computer science; Real-time computing; Reliability engineering; Electrical engineering","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":[0.0007271879,0.0001611761,0.0003274686,0.00002803065,0.0001334576,0.00004493392,0.00029382,0.00008712563,0.00002119204],"category_scores_gemma":[0.00004582663,0.0001273559,0.0002341996,0.0004036066,0.00003975484,0.0001695358,0.00004152919,0.000283095,0.00002864929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038028,"about_ca_system_score_gemma":0.0001498002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000225645,"about_ca_topic_score_gemma":0.00001143296,"domain_scores_codex":[0.9986798,0.0001594692,0.0003206059,0.0001583047,0.0002520184,0.0004297707],"domain_scores_gemma":[0.9990891,0.0001942166,0.0001113561,0.000273235,0.0002578314,0.00007420985],"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":[0.00008779806,0.000154533,0.006921277,0.0008798927,0.002855351,0.000003034744,0.08473381,0.7827519,0.02062717,0.01753024,0.01304715,0.07040785],"study_design_scores_gemma":[0.001776706,0.0001512355,0.02457999,0.0001557404,0.00005450996,0.000003725445,0.01066204,0.9548126,0.001427377,0.001865909,0.004180885,0.0003292302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7511278,0.001921816,0.2210223,0.009636619,0.003353722,0.002852294,0.0004366291,0.003157291,0.006491445],"genre_scores_gemma":[0.9985924,0.0001394465,0.0001411797,0.00008881103,0.0001614898,0.00002216553,0.000007091431,0.00002572846,0.0008216402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2474646,"threshold_uncertainty_score":0.5193422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03094948058450474,"score_gpt":0.3036137827301791,"score_spread":0.2726643021456744,"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."}}