{"id":"W2799875335","doi":"10.1016/j.vehcom.2018.04.002","title":"Quality of service aware multicasting in heterogeneous vehicular networks","year":2018,"lang":"en","type":"article","venue":"Vehicular Communications","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multicast; Computer network; Computer science; Protocol Independent Multicast; Source-specific multicast; Xcast; Network packet; Quality of service; Tree (set theory); Distance Vector Multicast Routing Protocol; Distributed computing","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":[0.0007668101,0.000276905,0.0004481003,0.0001564448,0.0001854041,0.00003600095,0.001261527,0.0002451268,0.00003724021],"category_scores_gemma":[0.00009251913,0.0003144272,0.0001459752,0.0009904383,0.0002167337,0.0001425139,0.0004634207,0.0005604436,0.00005929589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001308721,"about_ca_system_score_gemma":0.0000348072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003476627,"about_ca_topic_score_gemma":0.003980275,"domain_scores_codex":[0.9977071,0.0003933158,0.0008360737,0.0002726803,0.0002686468,0.0005221708],"domain_scores_gemma":[0.9963628,0.0003105967,0.0001405207,0.002736448,0.0003135889,0.0001360497],"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":[0.000006502876,0.00008791412,0.002662622,0.00006783893,0.00008554174,0.000005973893,0.0003259118,0.9916584,0.002328142,0.0002857432,0.00005749411,0.002427906],"study_design_scores_gemma":[0.0004446472,0.00002048647,0.0075821,0.0001547611,0.00002539379,0.0000179221,0.00008136427,0.9879672,0.0007506223,0.00009016762,0.002549997,0.0003153572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649202,0.004327576,0.02733144,0.0005168776,0.000219333,0.0006213982,0.00002366915,0.0004888668,0.001550677],"genre_scores_gemma":[0.9926325,0.0002612993,0.00654876,0.0001888759,0.0001072717,0.00006974482,0.0001081067,0.00007608724,0.00000736758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02771233,"threshold_uncertainty_score":0.9999308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03997942818605724,"score_gpt":0.2896687493284755,"score_spread":0.2496893211424182,"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."}}