{"id":"W2048926873","doi":"10.1145/2656346.2656411","title":"Adapting to the driving context in congestion control for vehicular networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer network; Network congestion; Quality of service; Scalability; Probabilistic logic; Context (archaeology); Overhead (engineering); Network packet; Distributed computing; Bottleneck; Vehicular ad hoc network; Node (physics); Wireless ad hoc network; Wireless; Engineering; 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":[0.001065984,0.0005529681,0.0004739001,0.000570997,0.0007501824,0.0007019613,0.001293429,0.0003635343,0.0003142252],"category_scores_gemma":[0.003497678,0.0003313409,0.000360106,0.0004605282,0.0006148528,0.0008677544,0.0009676564,0.0005827933,0.00005281858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007745864,"about_ca_system_score_gemma":0.0009584561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007788278,"about_ca_topic_score_gemma":0.009196374,"domain_scores_codex":[0.9993253,0.0002289158,0.00004359504,0.0001276974,0.0001961351,0.00007842289],"domain_scores_gemma":[0.9990086,0.0005060449,0.0001405497,0.0001038292,0.0001805501,0.00006052904],"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":[0.0001307238,0.00008196301,0.002515972,0.00009096529,0.00004630848,0.00009471684,0.0001875415,0.8906366,0.009046439,0.0105516,0.0002999108,0.08631722],"study_design_scores_gemma":[0.000008723847,0.0000515992,0.0002943837,0.000004322767,0.0000116321,0.00002525627,0.00001736524,0.9954482,0.001795411,0.001864279,0.000466831,0.00001199861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09326428,0.000504327,0.9043105,0.0001138197,0.00005160217,0.0001142243,0.00003135019,0.0002940283,0.001315787],"genre_scores_gemma":[0.9281574,0.0001660522,0.07111014,0.00003367145,0.00002321949,0.00005370425,0.00002986639,0.00002414438,0.0004017703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007788278,"threshold_uncertainty_score":0.01548588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005512024505473626,"score_gpt":0.1909184533785801,"score_spread":0.1854064288731065,"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."}}