{"id":"W2462138333","doi":"10.1109/tmc.2016.2582482","title":"Utility Maximization for Multimedia Data Dissemination in Large-Scale VANETs","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Dissemination; Scalability; Wireless ad hoc network; Computer network; Quality of service; Maximization; Path (computing); Taxis; Distributed computing; Multimedia; Telecommunications; Wireless; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004237706,0.000188866,0.0001995708,0.0001382389,0.0001127169,0.00002798483,0.0002751354,0.0001299969,0.00006089346],"category_scores_gemma":[0.00001416539,0.0001755528,0.00005825278,0.0002484088,0.00002427704,0.0002782522,0.000004830285,0.0001753717,0.00002330383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079756,"about_ca_system_score_gemma":0.00001516167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004881132,"about_ca_topic_score_gemma":0.000259532,"domain_scores_codex":[0.9986175,0.0000474035,0.0003585019,0.0004036094,0.0001604287,0.0004125052],"domain_scores_gemma":[0.9988002,0.0004604078,0.00004457451,0.0005658712,0.00004955842,0.00007944771],"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":[0.000009796902,0.00008066191,0.0001133655,0.00003167317,0.00001282832,0.000001152974,0.0001572008,0.7159725,0.001147213,8.645243e-7,0.0002196348,0.2822531],"study_design_scores_gemma":[0.0007234028,0.00002883282,0.0007775039,0.0001715779,0.00001834056,0.000003596365,0.00004799099,0.9912629,0.005505634,0.00001526478,0.00124369,0.0002013368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1128737,0.00006553795,0.8850231,0.00002499549,0.0007517038,0.0006749173,0.000211296,0.0003238104,0.00005098101],"genre_scores_gemma":[0.9892179,0.00004489799,0.01035265,0.00001065677,0.00009953814,0.00006294432,0.0001016389,0.00005209013,0.00005769812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8763441,"threshold_uncertainty_score":0.7158836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497876636128149,"score_gpt":0.2626810506432161,"score_spread":0.2477022842819346,"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."}}