{"id":"W2885335734","doi":"10.1007/978-3-030-01731-6_2","title":"Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals","year":2018,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Spoofing attack; Enhanced Data Rates for GSM Evolution; Computer network; Node (physics); Edge computing; Vehicular ad hoc network; Wireless ad hoc network; Real-time computing; Wireless; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0004178479,0.001011358,0.001017497,0.0003804662,0.0001367293,0.0001190333,0.0004053347,0.001319364,0.0004549876],"category_scores_gemma":[0.000006486139,0.001044784,0.000231023,0.0002178955,0.0001889251,0.0001235974,0.00006634358,0.002441641,0.0002022033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005880129,"about_ca_system_score_gemma":0.00007402236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003043031,"about_ca_topic_score_gemma":0.001873448,"domain_scores_codex":[0.996761,0.00008860429,0.0007069431,0.0008357106,0.0005439185,0.001063837],"domain_scores_gemma":[0.9984618,0.00015826,0.0002590364,0.0007064067,0.0001227927,0.0002916535],"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":[0.0001049877,0.00001908627,0.00004237375,0.0001034527,0.0001711028,0.0002382134,0.0000701041,0.9803772,0.00004387221,0.00005267888,0.001906431,0.01687051],"study_design_scores_gemma":[0.0009965916,0.0003670603,0.00007846903,0.001375397,0.0001118774,0.00004644445,0.000007185665,0.9428567,0.0002380029,0.00006895618,0.0527112,0.001142067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1133126,0.01975236,0.6815954,0.0001013429,0.008940953,0.007457178,0.00007138892,0.007791955,0.1609768],"genre_scores_gemma":[0.9801692,0.0004813065,0.0001361707,0.00007448294,0.002061403,0.000112782,0.0003134688,0.0006195245,0.0160317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8668565,"threshold_uncertainty_score":0.9999771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006129398983855332,"score_gpt":0.1786363547151625,"score_spread":0.1725069557313072,"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."}}