{"id":"W3130070797","doi":"10.1109/vtc2020-fall49728.2020.9348488","title":"Dynamic Spectrum Slicing and Optimization in SAG Integrated Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Lyapunov optimization; Heuristics; Queueing theory; Queue; Mathematical optimization; Scheduling (production processes); Distributed computing; Real-time computing; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.00002853657,0.0000695366,0.00007406006,0.00003848797,0.00001887192,0.00002781997,0.00003451105,0.00004839833,0.00005480728],"category_scores_gemma":[0.000004713425,0.00006873326,0.000008608488,0.0003391528,0.000006814399,0.00007693799,0.00001034908,0.00008600662,0.000003396878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002970196,"about_ca_system_score_gemma":0.000003050219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002345317,"about_ca_topic_score_gemma":0.00004992024,"domain_scores_codex":[0.9996513,0.000005944394,0.0001170959,0.0001017336,0.00003084683,0.0000930541],"domain_scores_gemma":[0.9998783,0.000008082799,0.000009301849,0.00005674332,0.000008343208,0.00003921103],"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":[0.000001341255,0.000002461316,0.0002586514,0.000006205655,0.00000336542,5.322543e-7,0.00007410518,0.9985634,0.00007897659,0.0001668991,0.00005395951,0.0007901163],"study_design_scores_gemma":[0.0001370904,0.000007518885,0.0005098938,0.000007407323,0.000003646438,7.667793e-7,0.0000530668,0.9989851,0.00005007709,0.00001441807,0.0001525957,0.00007844048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0213896,0.0001361053,0.9768784,0.0004011923,0.00001918845,0.0001225793,6.650317e-7,0.0002034233,0.000848818],"genre_scores_gemma":[0.9630432,0.0003046469,0.03642841,0.0001195117,0.00001350606,0.00001040916,0.00004745011,0.00001818908,0.0000147477],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9416535,"threshold_uncertainty_score":0.2802861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003453423770364256,"score_gpt":0.1709537648686327,"score_spread":0.1675003410982684,"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."}}