{"id":"W4399291542","doi":"10.3390/drones8060238","title":"Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment","year":2024,"lang":"en","type":"article","venue":"Drones","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Markov process; Human–computer interaction; Aeronautics; Aerospace engineering; Real-time computing; Simulation; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001721784,0.001103178,0.00110941,0.0007623088,0.0007638074,0.001371978,0.001406902,0.00124996,0.002144453],"category_scores_gemma":[0.004781549,0.0005593917,0.0008227551,0.0005693291,0.001657789,0.001809524,0.001313563,0.001101754,0.0001490692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003033105,"about_ca_system_score_gemma":0.002251919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01550539,"about_ca_topic_score_gemma":0.008412473,"domain_scores_codex":[0.9986709,0.000452566,0.00002945254,0.0002057895,0.0002703162,0.000371008],"domain_scores_gemma":[0.9961649,0.002404923,0.0006380402,0.00009743218,0.0004566374,0.000238099],"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":[0.00004297228,0.0000296877,0.000536163,0.00002991795,0.00002633852,0.0001327622,0.00006479512,0.9695118,0.0006948808,0.02691599,0.0003312119,0.001683353],"study_design_scores_gemma":[0.000003385676,0.00001334478,0.00008270777,0.000002125502,0.000004521627,0.00001095609,0.00001980457,0.9965575,0.0000863716,0.003146306,0.00006914151,0.000003809828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1758378,0.0004498503,0.8117515,0.0007327176,0.00006975519,0.0001405985,0.0001481865,0.0001084291,0.0107612],"genre_scores_gemma":[0.9884399,0.0001953806,0.008144483,0.00005653366,0.00001652817,0.00005628888,0.00002735939,0.00001548436,0.003048009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01550539,"threshold_uncertainty_score":0.03083032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02279859290811464,"score_gpt":0.246819946284857,"score_spread":0.2240213533767424,"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."}}