{"id":"W2973372523","doi":"10.1109/tvt.2019.2921444","title":"Toward Dynamic Link Utilization for Efficient Vehicular Edge Content Distribution","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Backhaul (telecommunications); Computer science; Computer network; Scheduling (production processes); Vehicular ad hoc network; Edge computing; Content delivery; Content distribution; Base station; Enhanced Data Rates for GSM Evolution; Wireless; Wireless ad hoc network; Engineering; Telecommunications","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":[0.0002179064,0.0002340349,0.0002781082,0.0003353622,0.0002230465,0.0000691168,0.0006208562,0.0003552293,0.000008019335],"category_scores_gemma":[0.00001442568,0.0002287611,0.0002561766,0.0006277584,0.0000744768,0.0001333635,0.000008634988,0.0003660843,0.0001452987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002288789,"about_ca_system_score_gemma":0.0000598257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001546916,"about_ca_topic_score_gemma":0.000006589783,"domain_scores_codex":[0.9983269,0.00004869106,0.0003208493,0.0006460302,0.0002567285,0.0004008269],"domain_scores_gemma":[0.9987277,0.00006487869,0.0001038902,0.0007994092,0.0002383038,0.00006585304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001856862,0.001175927,0.00009253467,0.0001970911,0.0003623457,0.00004996706,0.000253157,0.388478,0.1602882,0.05416716,0.0001500917,0.3945998],"study_design_scores_gemma":[0.001155899,0.0004442454,0.00008062236,0.00006572782,0.00004662282,0.00005001801,0.00007180542,0.9448153,0.04955528,0.0005485124,0.002856721,0.000309267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2596567,0.0001760475,0.7351798,0.002662425,0.001018248,0.000643037,0.00003960434,0.0006113539,0.00001279154],"genre_scores_gemma":[0.9981935,0.00004374003,0.001120479,0.0001605095,0.00001589358,0.000181499,0.00003576063,0.00002036616,0.0002282491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7385368,"threshold_uncertainty_score":0.9328609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013353884836637,"score_gpt":0.242981980017777,"score_spread":0.2128484411694106,"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."}}