{"id":"W2155496247","doi":"10.1109/glocom.2007.96","title":"Performance Enhancement for Secure Vehicular Communications","year":2007,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer network; Message authentication code; Overhead (engineering); Hash-based message authentication code; Latency (audio); Network packet; Packet loss; Authentication (law); Hash function; Low latency (capital markets); Protocol (science); Cryptographic protocol; Computer security; Cryptography; Telecommunications","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":[0.001113414,0.0009989762,0.0006830938,0.0009403299,0.0008915077,0.001357702,0.0006969735,0.0006292886,0.004132241],"category_scores_gemma":[0.003388921,0.0002115428,0.0002881495,0.0009092158,0.0004449043,0.002082058,0.001591493,0.0009432319,0.001747582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007073201,"about_ca_system_score_gemma":0.0008129939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006086021,"about_ca_topic_score_gemma":0.0006535181,"domain_scores_codex":[0.998341,0.000350362,0.00009534259,0.000159448,0.0007581143,0.0002957422],"domain_scores_gemma":[0.9978758,0.0006867491,0.0001917468,0.0004306646,0.0007214685,0.00009351048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001909013,0.0002555629,0.003315954,0.0007646838,0.0001261512,0.0009108652,0.0003444505,0.2381599,0.1586979,0.109721,0.01489155,0.4709031],"study_design_scores_gemma":[0.0000787849,0.001080139,0.001018478,0.00005202132,0.0001135666,0.0015867,0.0002336478,0.8693194,0.06491701,0.02255553,0.03896302,0.0000816977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08104044,0.006008803,0.880796,0.001039311,0.0007498157,0.0001951957,0.0001120634,0.003984682,0.02607377],"genre_scores_gemma":[0.9567317,0.001506915,0.03666918,0.0001115792,0.0002118353,0.00007326705,0.00016417,0.00008262238,0.004448782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004132241,"threshold_uncertainty_score":0.01382375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01253513183532045,"score_gpt":0.2367210721299751,"score_spread":0.2241859402946546,"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."}}