{"id":"W2587594232","doi":"10.1109/tnet.2017.2650964","title":"Software Defined Cooperative Offloading for Mobile Cloudlets","year":2017,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Tsinghua National Laboratory for Information Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Energy consumption; Distributed computing; Knapsack problem; Scheduling (production processes); Mobile device; Software; Embedded system; Operating system; Mathematical optimization; Algorithm","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":[0.0003756824,0.0002637731,0.000284195,0.0001137399,0.003173089,0.0007505687,0.001634561,0.0001266481,0.000004154807],"category_scores_gemma":[0.00004749076,0.0002677046,0.0001979261,0.0001847897,0.00006953556,0.0005280057,0.00003668557,0.0003185034,0.00003942228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009634794,"about_ca_system_score_gemma":0.00008313342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002375842,"about_ca_topic_score_gemma":0.00001525663,"domain_scores_codex":[0.9982234,0.00005155251,0.00030571,0.0005917846,0.000212104,0.0006155082],"domain_scores_gemma":[0.9976587,0.0005405721,0.0001946609,0.001339398,0.0001414049,0.0001252402],"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":[0.00005040602,0.0001308354,0.0001748494,0.00003644306,0.0001104138,0.00002313254,0.001386972,0.04626178,0.0004191739,0.0002416609,0.003677607,0.9474867],"study_design_scores_gemma":[0.003501336,0.001055729,0.0005547235,0.001020284,0.0001319658,0.000105329,0.00007242608,0.782234,0.01710362,0.007541487,0.1846595,0.002019722],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009081806,0.0001123365,0.963811,0.0003011155,0.02543725,0.0004764606,0.000001874645,0.0003678948,0.0004102552],"genre_scores_gemma":[0.8827676,0.00003266162,0.1120651,0.0003280316,0.004081776,0.0001880901,0.000003071974,0.00004495905,0.0004886492],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.945467,"threshold_uncertainty_score":0.9999775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543292871141028,"score_gpt":0.2943738331846961,"score_spread":0.2489409044732858,"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."}}