{"id":"W2540923637","doi":"10.1109/tvt.2016.2622180","title":"A Reinforcement Learning Technique for Optimizing Downlink Scheduling in an Energy-Limited Vehicular Network","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Scheduling (production processes); Computer science; Computer network; Telecommunications link; Quality of service; Real-time computing; Distributed computing; Engineering; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053668,0.0005485331,0.0005894809,0.0009929161,0.0002923511,0.0000457957,0.0005016887,0.001040406,0.00003971804],"category_scores_gemma":[0.00002045189,0.0005075223,0.000250992,0.001227301,0.0001411082,0.0003210125,0.000007978369,0.0009719289,0.00002040078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004849645,"about_ca_system_score_gemma":0.00004971545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001184238,"about_ca_topic_score_gemma":0.0001152901,"domain_scores_codex":[0.9969503,0.0001024187,0.000726507,0.0007128425,0.0002738397,0.001234062],"domain_scores_gemma":[0.9986311,0.0001482394,0.00010848,0.0008104831,0.0001299365,0.0001717957],"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":[0.00005313273,0.00006041451,0.00002966341,0.00003296803,0.0001122211,0.00004512023,0.00002376937,0.8789356,0.09997892,0.0005687014,0.00001884802,0.0201406],"study_design_scores_gemma":[0.001394744,0.0004094295,0.000005511525,0.0004497437,0.00006617525,0.00006358232,0.00004497302,0.8177586,0.1750561,0.0006310095,0.003483666,0.000636442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06800034,0.0003703329,0.9279884,0.0003510318,0.0003729272,0.0009137206,0.00000571578,0.001940449,0.00005712983],"genre_scores_gemma":[0.9575778,0.0004046637,0.03968643,0.00008810652,0.0001246766,0.00184572,0.00001814965,0.0001826444,0.00007184713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8895774,"threshold_uncertainty_score":0.9997376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00916894226873928,"score_gpt":0.2186064273133832,"score_spread":0.2094374850446439,"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."}}