{"id":"W7117152475","doi":"10.1016/j.trd.2025.105178","title":"Resilience analysis of electric vehicle charging infrastructure: a Bayesian network approach","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Science Basic Research Program of Shaanxi Province; Fundamental Research Funds for the Central Universities; Social Science Foundation of Shaanxi Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Resilience (materials science); Key (lock); Electric vehicle; Service (business); Bayesian network; Sensitivity (control systems); Critical infrastructure","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003185671,0.001071081,0.001771565,0.002882491,0.0007523671,0.001650634,0.002415109,0.001703113,0.004155212],"category_scores_gemma":[0.01251548,0.001384531,0.001571446,0.001767015,0.001845442,0.00346116,0.001772325,0.00165162,0.0002657809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002575499,"about_ca_system_score_gemma":0.001440756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02071957,"about_ca_topic_score_gemma":0.01137547,"domain_scores_codex":[0.9991048,0.00036314,0.00003170166,0.0002281712,0.000122178,0.0001500363],"domain_scores_gemma":[0.9927624,0.005615401,0.0007941314,0.0002186775,0.0004077594,0.0002017601],"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":[0.00002010862,0.00001030971,0.0005632736,0.00001591433,0.00003197352,0.00002995216,0.00001889628,0.9788976,0.0001181215,0.01773986,0.0002605826,0.002293463],"study_design_scores_gemma":[0.000002920418,0.000005484009,0.0002292557,0.000006391975,0.00001209008,0.000009320735,0.00001091592,0.9858842,0.00004929352,0.01367544,0.0001076238,0.00000709649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07529273,0.000472859,0.9180168,0.0009199486,0.00004206066,0.00006345158,0.0003749312,0.0002438516,0.004573422],"genre_scores_gemma":[0.9656726,0.0006852762,0.02648276,0.00009597378,0.00007875345,0.00008649201,0.0003579535,0.00007974319,0.006460373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02071957,"threshold_uncertainty_score":0.0411979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008295443658509533,"score_gpt":0.2327130591328635,"score_spread":0.224417615474354,"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."}}