{"id":"W4386473147","doi":"10.1109/tnse.2023.3312369","title":"Intelligent Content Caching and User Association in Mobile Edge Computing Networks for Smart Cities","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Western University","funders":"Chongqing Research Program of Basic Research and Frontier Technology","keywords":"Computer science; Latency (audio); Computer network; Association (psychology); Enhanced Data Rates for GSM Evolution; Handover; Frame (networking); Distributed computing; Artificial intelligence; Telecommunications","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":[0.001286004,0.0001151264,0.0001405563,0.0002419462,0.0003759442,0.0002477969,0.0001964119,0.00004796932,2.758833e-7],"category_scores_gemma":[0.00001803033,0.0001180088,0.00003586137,0.000865399,0.00003296482,0.0003947063,0.00001101602,0.0001983181,0.000001148004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001380055,"about_ca_system_score_gemma":0.00003495862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005161445,"about_ca_topic_score_gemma":0.00003553277,"domain_scores_codex":[0.9988348,0.00001527839,0.0001860702,0.0003125836,0.0002091673,0.0004420659],"domain_scores_gemma":[0.9993425,0.000350276,0.00003662284,0.0001268833,0.00006319126,0.00008053342],"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":[0.00000317091,0.000007428134,0.000419842,0.00001007412,0.000006876466,0.000001312442,0.0003328383,0.9754905,0.0003093861,0.0003081477,0.00006409324,0.02304632],"study_design_scores_gemma":[0.0001912897,0.00005220577,0.001809388,0.00009349024,0.000004722425,0.000003390456,0.0001156917,0.9970932,0.000157629,0.00001696709,0.0003272118,0.0001348307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2810587,0.0001599014,0.7173869,0.00008294052,0.0009996118,0.0001568921,9.603862e-7,0.0001447256,0.000009355644],"genre_scores_gemma":[0.9984881,0.0002439842,0.0009607335,0.00009556922,0.00007488038,0.00004044834,4.771368e-7,0.000007865598,0.00008789903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7174295,"threshold_uncertainty_score":0.481226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0219091136189821,"score_gpt":0.2218230306873447,"score_spread":0.1999139170683626,"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."}}