{"id":"W4408323489","doi":"10.1109/ojvt.2025.3550307","title":"Cyber Threat Susceptibility Assessment for Heavy-Duty Vehicles Based on ISO/SAE 21434","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of Vehicular Technology","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Mitacs","keywords":"Heavy duty; Computer security; Aeronautics; Environmental science; Computer science; Automotive engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001338992,0.0002127149,0.0004890259,0.0004794236,0.0001343254,0.00005723328,0.0005877635,0.0002653547,0.000024537],"category_scores_gemma":[0.0001639356,0.0001858069,0.0001861872,0.0004790092,0.00008514085,0.000181037,0.00004299308,0.0005726541,0.000003454764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003017652,"about_ca_system_score_gemma":0.0001178285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002500079,"about_ca_topic_score_gemma":0.00002487375,"domain_scores_codex":[0.9986272,0.00008298278,0.0005500051,0.0002308374,0.0002206686,0.0002882917],"domain_scores_gemma":[0.9988573,0.000152835,0.0001580311,0.0004499994,0.0003188855,0.00006296559],"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":[0.0006754373,0.0004837369,0.005634899,0.0002277676,0.0004608832,0.00005424591,0.00002225273,0.2110389,0.5145428,0.002767027,0.004362969,0.2597291],"study_design_scores_gemma":[0.005150624,0.001429067,0.00407438,0.0004112061,0.0002063145,0.00003640541,0.0001944116,0.03231151,0.8864803,0.02888282,0.04035966,0.0004632593],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2092467,0.0004306729,0.7840348,0.001790787,0.001368169,0.0007542365,0.00000729986,0.0001700793,0.002197193],"genre_scores_gemma":[0.8662266,0.00004004322,0.1332771,0.0001959642,0.00006874548,0.0000533878,0.000001224116,0.00002812372,0.0001088155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6569799,"threshold_uncertainty_score":0.7576987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02786980858731667,"score_gpt":0.3453297320836453,"score_spread":0.3174599234963286,"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."}}