{"id":"W2949570395","doi":"10.1145/3331052.3332476","title":"Sec-IoV","year":2019,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Support vector machine; Anomaly detection; Heuristic; Intelligent transportation system; Rate of convergence; Data mining; Convergence (economics); Differential evolution; Artificial intelligence; Channel (broadcasting); Computer network; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001338021,0.001347306,0.0007908865,0.0008492359,0.000762566,0.002538458,0.002484724,0.001918749,0.1228464],"category_scores_gemma":[0.002981044,0.0004277013,0.0008530685,0.0007620262,0.0006313815,0.002569042,0.002685387,0.001463529,0.08908968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009589928,"about_ca_system_score_gemma":0.001893355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002558213,"about_ca_topic_score_gemma":0.002400577,"domain_scores_codex":[0.9989249,0.000185732,0.00006435055,0.0001927815,0.0004750765,0.0001571427],"domain_scores_gemma":[0.9986972,0.0001501536,0.00007666555,0.0005399944,0.0004082079,0.000127785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005685075,0.0002603423,0.003729236,0.0006159657,0.0000926197,0.000521158,0.0001705927,0.03402855,0.008616499,0.07469683,0.6028961,0.2738035],"study_design_scores_gemma":[0.00009013851,0.0001836672,0.0008345363,0.00008732909,0.00001878987,0.0003824471,0.00005303681,0.1087003,0.007093071,0.01884881,0.8636653,0.00004254319],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01853094,0.002419071,0.3525624,0.003188224,0.004896194,0.001293465,0.02229271,0.1588616,0.4359555],"genre_scores_gemma":[0.2569927,0.003073992,0.1820272,0.003172391,0.001112051,0.001562425,0.1044252,0.02504054,0.4225935],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8771536,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001334187962614722,"score_gpt":0.1372182409989749,"score_spread":0.1358840530363601,"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."}}