{"id":"W2908049050","doi":"10.1109/smartgridcomm.2018.8587476","title":"Deep Q-Learning for Low-Latency Tactile Applications: Microgrid Communications","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Microgrid; Latency (audio); Computer network; Enabling; Quality of service; Distributed computing; User equipment; Wireless; Base station; Low latency (capital markets); Resource allocation; Wireless network; Control (management); Artificial intelligence; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0000985064,0.0001125586,0.0001043779,0.00004856737,0.0002599731,0.00004405711,0.0004075898,0.00008535406,0.00007314877],"category_scores_gemma":[0.00003149699,0.0001208865,0.00004293838,0.0001963075,0.00009454494,0.00009936325,0.00006477055,0.0001701412,0.0001378855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004007306,"about_ca_system_score_gemma":0.00000745328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001196772,"about_ca_topic_score_gemma":0.00008425171,"domain_scores_codex":[0.9993563,0.00001631147,0.0002000299,0.0001379927,0.00005265031,0.0002367026],"domain_scores_gemma":[0.9989668,0.00023334,0.0000335388,0.0005979701,0.0001103835,0.00005801262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001266908,0.0001802173,0.002458161,0.0002816301,0.0002377504,5.925233e-7,0.0007016608,0.4167095,0.01989221,0.04244911,0.01409119,0.5029853],"study_design_scores_gemma":[0.0001362345,0.00002205809,0.0002033972,0.00002673343,0.00001225186,0.00000290929,0.00004460932,0.7269337,0.002777949,0.0003433195,0.2693197,0.0001770692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003585638,0.001400851,0.9481816,0.00009167027,0.000221956,0.0004337763,0.000002145155,0.001638345,0.04444399],"genre_scores_gemma":[0.8565871,0.0004794034,0.1406591,0.00007617201,0.0004879802,0.000781122,0.00007502565,0.00007339203,0.0007806541],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8530015,"threshold_uncertainty_score":0.4929607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094044794738726,"score_gpt":0.2398400375998017,"score_spread":0.2288995896524144,"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."}}