{"id":"W4385444829","doi":"10.1109/tmc.2023.3300311","title":"Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cell Transmission Model; Controller (irrigation); Efficient energy use; Traffic flow (computer networking); Optimization problem; Power control; Real-time computing; Computer network; Power (physics); Traffic congestion; Engineering; Algorithm","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":[0.0007085747,0.0007262165,0.0007510572,0.0003862011,0.0003756269,0.000761668,0.0008201829,0.000514151,0.0005545444],"category_scores_gemma":[0.0009817516,0.0002904559,0.0004732494,0.0004318143,0.0005532024,0.00083974,0.0008237314,0.0005275795,0.00007191559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001034067,"about_ca_system_score_gemma":0.00103999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005732779,"about_ca_topic_score_gemma":0.00477935,"domain_scores_codex":[0.9996569,0.0000882549,0.00001112438,0.00008774792,0.00008868469,0.00006731942],"domain_scores_gemma":[0.9996688,0.0001470235,0.00006840697,0.00002521178,0.0000668236,0.00002373463],"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":[0.00001944796,0.000009957621,0.0001889788,0.00001247533,0.000008179324,0.00001291997,0.00001738692,0.9843374,0.001203756,0.003449409,0.0002065237,0.01053354],"study_design_scores_gemma":[0.00000192636,0.0000084154,0.00004781495,8.390224e-7,0.000002092528,0.000002725749,0.000003476202,0.9986615,0.0002148625,0.0009415575,0.0001132331,0.000001386651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01986275,0.0001494978,0.9782711,0.00006892704,0.00001655129,0.00001586079,0.00001937365,0.0001279216,0.001468049],"genre_scores_gemma":[0.9605073,0.000127665,0.03816351,0.00003547988,0.00002141257,0.00004610045,0.00004709352,0.00003352619,0.001017918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005732779,"threshold_uncertainty_score":0.01139879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896312022246601,"score_gpt":0.2298360013454996,"score_spread":0.2108728811230336,"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."}}