{"id":"W4400277304","doi":"10.1109/wcnc57260.2024.10570939","title":"Maximizing Group-Based Vehicle Communications and Fairness: A Reinforcement Learning Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Concordia University; Lakehead University","funders":"","keywords":"Reinforcement learning; Computer science; Reinforcement; Artificial intelligence; Engineering","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":[0.00267074,0.0008211753,0.001224127,0.0005020935,0.000581892,0.0007717877,0.001550102,0.001089774,0.001544627],"category_scores_gemma":[0.005355952,0.0003437703,0.0003671556,0.0003888376,0.001428837,0.001255656,0.0009896018,0.001107618,0.0001762947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001370477,"about_ca_system_score_gemma":0.001746511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006080456,"about_ca_topic_score_gemma":0.003960199,"domain_scores_codex":[0.9990371,0.0004237143,0.0000313975,0.0001877311,0.0001411791,0.0001788673],"domain_scores_gemma":[0.9961649,0.002725476,0.0003857143,0.0001276695,0.0003741481,0.0002220398],"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":[0.00007251033,0.0000563685,0.0006029297,0.00002814403,0.00002102311,0.00003824022,0.00004300945,0.9765163,0.0005925404,0.006857884,0.0003431281,0.01482794],"study_design_scores_gemma":[0.000008068641,0.00002225106,0.00004214563,0.000002073994,0.000003017568,0.000005113971,0.000006891662,0.9971586,0.0001257856,0.002514164,0.0001093959,0.000002437491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04033392,0.0002559497,0.9564025,0.0003712921,0.00004951898,0.00006745218,0.00002356376,0.0001414702,0.002354357],"genre_scores_gemma":[0.9432088,0.0001152541,0.05454817,0.0001473506,0.0000385391,0.00008271054,0.00002342647,0.00002421468,0.001811577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006080456,"threshold_uncertainty_score":0.01412439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197118600598853,"score_gpt":0.2460989441406206,"score_spread":0.2263870840807353,"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."}}