{"id":"W3045897666","doi":"10.1109/icc40277.2020.9149168","title":"Load Balancing and QoS-Aware Network Selection Scheme in Heterogeneous Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Handover; Computer science; Quality of service; Computer network; Mobility management; Benchmark (surveying); Load balancing (electrical power); Heterogeneous network; Wireless; Wireless network; Process (computing); Software deployment; Vehicular ad hoc network; Distributed computing; Wireless ad hoc network; 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"],"consensus_categories":[],"category_scores_codex":[0.0002592158,0.0002812483,0.0003436906,0.00002927418,0.00007442368,0.00007279854,0.0001244172,0.0002449367,0.00008643827],"category_scores_gemma":[0.00002306517,0.0003073247,0.00006643965,0.0005108689,0.00002892988,0.0001614696,0.00009378511,0.0005202213,0.00001485851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002000876,"about_ca_system_score_gemma":0.00001961916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006393225,"about_ca_topic_score_gemma":0.0007363057,"domain_scores_codex":[0.9983728,0.00006054344,0.0003529606,0.0004077387,0.0002016426,0.0006043497],"domain_scores_gemma":[0.9994865,0.00006068833,0.00003282423,0.0001805481,0.0000461441,0.0001932872],"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":[0.00003075725,0.00001246739,0.02420034,0.00008753347,0.00003873448,0.00002627449,0.0001635323,0.97221,0.0002060876,0.00003097491,0.001685579,0.001307673],"study_design_scores_gemma":[0.0005529047,0.00004966761,0.002827636,0.00004319252,0.00001566874,0.00002057198,0.00002062039,0.9932691,0.0002839676,0.00004631684,0.00254764,0.0003227279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9209841,0.006348216,0.06901694,0.0001425161,0.001012902,0.0005946069,0.000002418255,0.001072687,0.0008255521],"genre_scores_gemma":[0.9970805,0.0002425954,0.0008447741,0.0004540096,0.001269989,0.00002655913,0.00001069402,0.0000569808,0.00001388195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07609636,"threshold_uncertainty_score":0.9999379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006294294488393568,"score_gpt":0.1834029112938901,"score_spread":0.1771086168054965,"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."}}