{"id":"W4402809523","doi":"10.1109/tmc.2024.3465591","title":"Multi-User Task Offloading in UAV-Assisted LEO Satellite Edge Computing: A Game-Theoretic Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Edge computing; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Game theory; Distributed computing; Human–computer interaction; Server; Computer network; Artificial intelligence; Systems 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"],"consensus_categories":[],"category_scores_codex":[0.001302085,0.0006398459,0.0006417891,0.001112101,0.0005441114,0.0009235166,0.001296462,0.0002714397,0.000006282741],"category_scores_gemma":[0.00001515487,0.000657014,0.0004031728,0.002753926,0.0001376845,0.0005193116,0.00005380069,0.001394911,0.0002048254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004023237,"about_ca_system_score_gemma":0.0001886218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005126759,"about_ca_topic_score_gemma":0.000005567984,"domain_scores_codex":[0.9952035,0.0003834216,0.001053183,0.001602318,0.0005294135,0.001228154],"domain_scores_gemma":[0.9977088,0.0008430329,0.00016225,0.0009155347,0.000116689,0.0002537066],"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":[0.00002840043,0.001028688,0.0001589434,0.0005712676,0.0001540296,0.0002440491,0.01418617,0.1904455,0.005234845,0.001382688,0.0002793072,0.7862861],"study_design_scores_gemma":[0.0007880539,0.0001428113,0.0006296574,0.0007643163,0.00003393893,0.0001974066,0.0001707731,0.9894623,0.002589953,0.0001016675,0.004377503,0.0007416824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07301799,0.0009386487,0.9118638,0.00009851846,0.01100671,0.000712504,0.000001547767,0.001530947,0.0008293378],"genre_scores_gemma":[0.9302181,0.00003799257,0.06834244,0.0001655705,0.0007864715,0.00003294077,0.000005062665,0.00008959842,0.0003217988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8572001,"threshold_uncertainty_score":0.9995881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02398179475272112,"score_gpt":0.2700767323897098,"score_spread":0.2460949376369887,"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."}}