{"id":"W4205196045","doi":"10.1109/ojcoms.2021.3135290","title":"Low-Complexity Resource Allocation for Dense Cellular Vehicle-to-Everything (C-V2X) Communications","year":2021,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Resource allocation; Scalability; Cellular network; Quality of service; Mathematical optimization; Optimization problem; Computational complexity theory; Channel (broadcasting); Computer network; Distributed computing; Algorithm; Mathematics","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00166856,0.0001941994,0.0003410882,0.00003638626,0.001071159,0.0003244554,0.006849159,0.0001297824,0.0000136643],"category_scores_gemma":[0.0001854554,0.0001817013,0.0004811576,0.0006295687,0.0002278836,0.0003933814,0.001606424,0.00089126,0.00001406727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003994757,"about_ca_system_score_gemma":0.0002401003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001736823,"about_ca_topic_score_gemma":0.0001631044,"domain_scores_codex":[0.9980317,0.0004890663,0.0007309927,0.0001504465,0.0002856959,0.0003121262],"domain_scores_gemma":[0.9937408,0.0006222617,0.0003199707,0.004481349,0.0006687505,0.0001668537],"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":[0.00002671555,0.0004008164,0.0001480465,0.00009556884,0.0006411571,0.000002115686,0.006443267,0.7972045,0.07345964,0.003580416,0.1129304,0.005067311],"study_design_scores_gemma":[0.00140153,0.00004781639,0.000565446,0.0006483928,0.0003099389,0.0001310994,0.002486045,0.6601225,0.02421159,0.00315452,0.306403,0.0005182192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4934121,0.02021865,0.3319577,0.1361644,0.002667533,0.005347321,0.0002430868,0.0004028344,0.009586322],"genre_scores_gemma":[0.8467134,0.0009767852,0.1504047,0.001235314,0.0001416232,0.00006540546,0.00004453812,0.00007264459,0.0003456192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3533013,"threshold_uncertainty_score":0.9985242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06462855490022049,"score_gpt":0.2951307418216423,"score_spread":0.2305021869214219,"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."}}