{"id":"W3196427268","doi":"10.1109/access.2021.3108425","title":"A Centralized and Dynamic Network Congestion Classification Approach for Heterogeneous Vehicular Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Network congestion; Support vector machine; Naive Bayes classifier; Network packet; Dynamic Bayesian network; Data mining; Artificial intelligence; Packet loss; Warning system; Machine learning; Bayesian network; Computer network","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":[0.0009926017,0.0007117704,0.0008850595,0.001207809,0.0007626154,0.000884777,0.001436979,0.0005631424,0.0007548152],"category_scores_gemma":[0.001905011,0.0003559958,0.0008041001,0.0007395392,0.0003960465,0.001402507,0.0006466234,0.0007133428,0.0001441056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001362329,"about_ca_system_score_gemma":0.001018251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01448058,"about_ca_topic_score_gemma":0.01263725,"domain_scores_codex":[0.9993035,0.0001232925,0.00004084438,0.0002217195,0.0001912166,0.0001194669],"domain_scores_gemma":[0.9994054,0.0001339559,0.0000900946,0.00006081676,0.0002594325,0.00005025373],"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":[0.0001338649,0.0001497218,0.005682075,0.00006316368,0.00009183699,0.0001544824,0.0001431049,0.753879,0.005124936,0.008682593,0.002199944,0.2236953],"study_design_scores_gemma":[0.000002144262,0.00001602373,0.0004362404,0.000001827411,0.000007435214,0.00001321849,0.00001267564,0.9979185,0.0002754666,0.001161746,0.0001487029,0.000005852363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0275797,0.0001261259,0.9707798,0.00009799586,0.00004215569,0.00006099475,0.00004521412,0.0003668894,0.0009011381],"genre_scores_gemma":[0.9080584,0.000139125,0.08959923,0.00005965877,0.00006525309,0.00009304348,0.0002098363,0.00003737997,0.001738063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01448058,"threshold_uncertainty_score":0.02879262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01982743493187627,"score_gpt":0.2570573317580185,"score_spread":0.2372298968261422,"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."}}