{"id":"W3035632193","doi":"10.1007/s11276-020-02385-1","title":"End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment","year":2020,"lang":"en","type":"article","venue":"Wireless Networks","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Computation offloading; Server; Enhanced Data Rates for GSM Evolution; Mobile edge computing; Distributed computing; Benchmark (surveying); Edge computing; Quality of service; Mobile device; Computer network; Operating system; Artificial intelligence","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":[0.0002511985,0.0002690375,0.0003497434,0.00006773332,0.0002174107,0.000173753,0.0005615693,0.0001434152,0.000003452417],"category_scores_gemma":[0.00001989521,0.0002914762,0.0001034184,0.0005135403,0.00005272435,0.0002695004,0.0003279687,0.0002336648,0.00002675196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563658,"about_ca_system_score_gemma":0.0000585216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001593705,"about_ca_topic_score_gemma":0.00001106707,"domain_scores_codex":[0.9979696,0.0001232096,0.0004350032,0.00067221,0.0002265487,0.0005734855],"domain_scores_gemma":[0.9989555,0.0003638609,0.0001837677,0.0002555929,0.00006010414,0.0001811149],"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":[0.00005517402,0.000083012,0.003506087,0.00003776597,0.00003573126,0.00002740119,0.003926819,0.9309028,0.000235557,0.000126062,0.003570767,0.05749283],"study_design_scores_gemma":[0.001031457,0.0001854389,0.0005378354,0.00004448687,0.000007097378,0.000003039072,0.00007317564,0.9903702,0.0008659395,0.00006277054,0.006492477,0.000326074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2990788,0.0003892975,0.6965414,0.0002103772,0.002865318,0.0007367204,0.000001566171,0.000136763,0.00003981057],"genre_scores_gemma":[0.9867505,0.00003416048,0.01069474,0.0005169744,0.001797892,0.0001233015,0.00003279721,0.00003340948,0.00001616443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6876718,"threshold_uncertainty_score":0.9999537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171948016925145,"score_gpt":0.2288166706288136,"score_spread":0.2116218689362991,"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."}}