{"id":"W4392843891","doi":"10.1109/tits.2024.3352668","title":"Secrecy Performance Intelligent Prediction for Mobile Vehicular Networks: An DI-CNN Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Computer science; Intelligent transportation system; Secrecy; Vehicular ad hoc network; Artificial intelligence; Computer security; Engineering; Wireless ad hoc network; Wireless; Telecommunications; Transport 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004609353,0.0005892113,0.0003592459,0.0003360305,0.0002323379,0.0004420553,0.000962733,0.0005071877,0.0009251341],"category_scores_gemma":[0.001117191,0.0002364305,0.0003455832,0.0002727703,0.0003582744,0.0007861684,0.0004740063,0.0006832819,0.0001594757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296643,"about_ca_system_score_gemma":0.0007333618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01282736,"about_ca_topic_score_gemma":0.008674116,"domain_scores_codex":[0.9998844,0.00001703549,0.000006297556,0.00003051773,0.00003354492,0.00002827833],"domain_scores_gemma":[0.9996835,0.000117871,0.00004708593,0.00002623225,0.0001071818,0.00001817186],"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":[0.00005347065,0.00001994821,0.001708952,0.00002565489,0.00001849192,0.0000479504,0.00002033072,0.9565096,0.002169252,0.004522425,0.000420206,0.03448372],"study_design_scores_gemma":[3.966911e-7,0.000003630672,0.0000425134,7.155281e-7,0.000001403441,0.000003208004,8.499482e-7,0.9993334,0.0002885898,0.0002927394,0.00003169573,7.264503e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1272538,0.0007920841,0.8649341,0.0005340733,0.0000771312,0.00003449501,0.0001365938,0.0005320744,0.005705609],"genre_scores_gemma":[0.9644765,0.0002919594,0.03256487,0.00008151487,0.00002291592,0.00002634567,0.0001219694,0.00002550996,0.002388464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01282736,"threshold_uncertainty_score":0.02550542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01720868185950114,"score_gpt":0.2305081001579447,"score_spread":0.2132994182984436,"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."}}