{"id":"W4402156372","doi":"10.1109/icc51166.2024.10622589","title":"Dual-timescales Optimization for Resource Slicing and Task Scheduling in Satellite Edge Computing Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Computer science; Slicing; Satellite; Scheduling (production processes); Processor scheduling; Task (project management); Distributed computing; Enhanced Data Rates for GSM Evolution; Dual (grammatical number); Resource (disambiguation); Computer network; Artificial intelligence; Mathematical optimization; World Wide Web; Systems engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0008116615,0.0001587564,0.0001808971,0.000208705,0.0001994039,0.0006408024,0.0002137339,0.00008692107,7.291371e-7],"category_scores_gemma":[0.0000401803,0.0001507382,0.00004850172,0.0005733198,0.00002735818,0.0003448052,0.0003156587,0.0001842493,0.000003036908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000414881,"about_ca_system_score_gemma":0.00003037236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001607378,"about_ca_topic_score_gemma":0.000002189669,"domain_scores_codex":[0.9985938,0.00005038679,0.0003295197,0.000505839,0.000108298,0.0004121531],"domain_scores_gemma":[0.9991288,0.0005343403,0.00004381214,0.0001872458,0.00003627178,0.00006957604],"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":[0.000003737608,0.00001056168,0.0004673236,0.00008984015,0.00001089829,0.00001196438,0.00167011,0.7508252,0.00006412627,0.004252914,0.0005576577,0.2420357],"study_design_scores_gemma":[0.0001664993,0.00002443211,0.0002128504,0.0003047905,0.000004757918,0.00001791992,0.00004332594,0.9944749,0.00006332788,0.0002622806,0.004234186,0.000190729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02488401,0.002445208,0.9684398,0.0005286854,0.001902901,0.0002087837,6.532372e-8,0.0003868739,0.001203716],"genre_scores_gemma":[0.4546155,0.00005353018,0.5427153,0.000330597,0.002084052,0.000004664735,0.000009091288,0.0000293732,0.0001578605],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4297315,"threshold_uncertainty_score":0.6179272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222413563794564,"score_gpt":0.2450147476795351,"score_spread":0.2327906120415895,"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."}}