{"id":"W2567632073","doi":"10.1109/camad.2016.7790331","title":"An efficient method for mobile big data transfer over HetNet in emerging 5G systems","year":2016,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Heterogeneous network; Big data; Distributed computing; Exploit; Wireless; Mobile device; Latency (audio); User equipment; Data transmission; Mobile cloud computing; Computer network; Wireless network; Base station; Data mining; Telecommunications; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001407202,0.000137767,0.0001970562,0.0001428986,0.0000860734,0.0001413806,0.001406709,0.00005437989,0.000003292806],"category_scores_gemma":[0.00001886588,0.00008981444,0.00003929543,0.000277361,0.00001337541,0.0003306281,0.0001895892,0.00005416613,0.00001284229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004087746,"about_ca_system_score_gemma":0.00004985446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002356253,"about_ca_topic_score_gemma":0.00001548522,"domain_scores_codex":[0.9983183,0.0001130707,0.0003057361,0.0006378624,0.0001960582,0.0004289786],"domain_scores_gemma":[0.9984395,0.0002769889,0.00002612556,0.001127889,0.00004085062,0.00008865777],"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":[0.00002333076,0.0003695732,0.002092395,0.0001331048,0.00003055313,0.0000150262,0.002911101,0.01200851,0.02020621,0.01135188,0.01026155,0.9405968],"study_design_scores_gemma":[0.0005889504,0.00007746617,0.0002318571,0.00005647312,0.000003379044,0.000005158055,0.00003744709,0.9649956,0.001427126,0.00005730557,0.03232411,0.0001951952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04471037,0.00009017472,0.9472171,0.0001282157,0.006960646,0.0003941392,0.000002989679,0.0001442421,0.00035214],"genre_scores_gemma":[0.9036809,0.000004467121,0.09352761,0.000172931,0.002143745,0.00006388625,0.00001000503,0.00002535419,0.0003710586],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.952987,"threshold_uncertainty_score":0.3662527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06454420471376067,"score_gpt":0.338128371544642,"score_spread":0.2735841668308813,"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."}}