{"id":"W4394805297","doi":"10.1109/tcomm.2024.3388501","title":"Multi-UAV Aided Multi-Access Edge Computing in Marine Communication Networks: A Joint System-Welfare and Energy-Efficient Design","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China; National Research Foundation","keywords":"Joint (building); Computer science; Enhanced Data Rates for GSM Evolution; Communications system; Efficient energy use; Telecommunications; Energy (signal processing); Computer network; Engineering; Electrical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0009886719,0.0003138526,0.0003372903,0.0006082427,0.001317921,0.0007675457,0.002364839,0.0001481226,0.00000253854],"category_scores_gemma":[0.00001258827,0.0003269191,0.0001253247,0.001432985,0.0001699911,0.0004710653,0.0002388188,0.0007811319,0.00001469692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003363466,"about_ca_system_score_gemma":0.00009892722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008011241,"about_ca_topic_score_gemma":0.0002755763,"domain_scores_codex":[0.9972736,0.0007857693,0.000731279,0.0005608397,0.0002091402,0.0004394367],"domain_scores_gemma":[0.99625,0.0007786764,0.000136379,0.002553488,0.000140382,0.0001410269],"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":[0.00001781893,0.00128851,0.00007971998,0.0002112458,0.0001415394,0.0000204578,0.004572918,0.75218,0.0003893177,0.008368667,0.0002890233,0.2324408],"study_design_scores_gemma":[0.0005182045,0.00003487488,0.0008998904,0.0005783132,0.00002705839,0.00004389203,0.0001426836,0.9961348,0.0002822214,0.00003395373,0.0009810927,0.0003229773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001272286,0.00153217,0.991917,0.001755734,0.002049684,0.0003739656,0.000002312264,0.0008144944,0.0002823508],"genre_scores_gemma":[0.8136174,0.0003426674,0.185674,0.00008353804,0.00005504756,0.0001090597,0.00001128699,0.00003332754,0.0000736701],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8123451,"threshold_uncertainty_score":0.9999822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07157588372520994,"score_gpt":0.2966938969345554,"score_spread":0.2251180132093454,"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."}}