{"id":"W3133982043","doi":"10.1109/tvt.2021.3065084","title":"UAV Communications for Sustainable Federated Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Research Foundation of Korea; Pusan National University","keywords":"Wireless; Leverage (statistics); Wireless power transfer; Wireless network; Transmitter power output; Scheduling (production processes); Transmission (telecommunications); Boosting (machine learning)","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":[0.00006261455,0.0001150626,0.0001316181,0.0002177458,0.0005074529,0.00004694362,0.0001687635,0.0002251027,0.0000395329],"category_scores_gemma":[0.00001175076,0.000137957,0.0000644579,0.0008090262,0.00005963628,0.00008367592,0.000002424508,0.0003378426,0.00002932929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009478969,"about_ca_system_score_gemma":0.0000368979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004814513,"about_ca_topic_score_gemma":0.00003177913,"domain_scores_codex":[0.9993402,0.00001891145,0.0001729699,0.000174211,0.00005481661,0.0002388532],"domain_scores_gemma":[0.9991426,0.0000502716,0.00002130678,0.0004789229,0.0002758168,0.00003110931],"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":[0.000005094918,0.0001395208,0.000008121847,0.00005226908,0.0001012474,0.00000705666,0.00004272349,0.9476596,0.01550146,0.0111462,0.0002935283,0.02504318],"study_design_scores_gemma":[0.000518549,0.00006191356,0.000006649411,0.00001686888,0.00006002969,0.00002995473,0.0008604039,0.6965411,0.1994766,0.001220448,0.1009613,0.0002462331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00890263,0.0003450038,0.9871172,0.001027724,0.00005806062,0.0003003087,0.000006751959,0.001120417,0.001121858],"genre_scores_gemma":[0.9788693,0.0004131896,0.01843048,0.00003889613,0.000008871414,0.0006448641,0.00004361735,0.00004296655,0.001507858],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9699667,"threshold_uncertainty_score":0.5625722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008019406256754853,"score_gpt":0.2229306386666972,"score_spread":0.2149112324099423,"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."}}