{"id":"W2598514907","doi":"10.1109/vtcfall.2016.7881584","title":"Tradeoffs in PRACH Bandwidth Partitioning for VM2M Overlay Network in LTE","year":2016,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer network; Overlay; Computer science; Overlay network; Vehicular ad hoc network; Physical layer; Bandwidth (computing); Software deployment; Channel (broadcasting); Access control; Wireless; Wireless ad hoc network; Telecommunications; The Internet; Operating system","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":[0.002122401,0.0008187122,0.0006045365,0.00100504,0.001302943,0.001453196,0.000984347,0.0007406659,0.0009419817],"category_scores_gemma":[0.00845243,0.0002277688,0.0002221995,0.0007691419,0.0008252485,0.002149689,0.0009714794,0.0004465724,0.0001303295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001829837,"about_ca_system_score_gemma":0.0007812417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007097732,"about_ca_topic_score_gemma":0.009883928,"domain_scores_codex":[0.99784,0.0008086886,0.00007077931,0.0001749694,0.0004453724,0.0006601668],"domain_scores_gemma":[0.9935793,0.004081313,0.0004657173,0.0004907391,0.001109497,0.0002734677],"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":[0.001347622,0.0004001496,0.01253834,0.0001993878,0.0001316616,0.000464572,0.0003910462,0.8648002,0.04282447,0.008736837,0.001143255,0.06702245],"study_design_scores_gemma":[0.00004537053,0.001682123,0.006926967,0.0000345361,0.0001223847,0.0005290373,0.001107931,0.9552727,0.02971113,0.003353802,0.001147873,0.00006611012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.941518,0.001375074,0.05098591,0.0002821752,0.00005927515,0.00008300407,0.00007260071,0.0002268008,0.005397201],"genre_scores_gemma":[0.9965772,0.00008578684,0.003162995,0.000015487,0.00000492618,0.00000893627,0.00001499512,0.000007314566,0.0001224713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007097732,"threshold_uncertainty_score":0.01411283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006966376622277887,"score_gpt":0.1970194596405862,"score_spread":0.1900530830183083,"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."}}