{"id":"W4392906046","doi":"10.32920/25413808.v1","title":"Latency Efficient Cache Placement Using Learning Techniques in Mobile Edge Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cache; Computer network; Mobile edge computing; Base station; Cache algorithms; Edge device; Enhanced Data Rates for GSM Evolution; Wireless network; Radio access network; Latency (audio); Distributed computing; Wireless; CPU cache; Server; Cloud computing; Mobile station; Telecommunications; Operating system","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":[0.0008534734,0.0003233974,0.0003398213,0.0003735026,0.00009377098,0.0004577641,0.0008612885,0.0002835465,0.00001547538],"category_scores_gemma":[0.00001391539,0.0002935547,0.0001796737,0.0003381114,0.00002590846,0.00004698298,0.004580585,0.00186303,0.00002531209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003773801,"about_ca_system_score_gemma":0.0001701726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007778801,"about_ca_topic_score_gemma":0.00002511303,"domain_scores_codex":[0.9977654,0.0001513581,0.000440911,0.0009050748,0.0003104016,0.0004268636],"domain_scores_gemma":[0.9990527,0.00006775615,0.0001135563,0.0006307322,0.00006024083,0.00007504024],"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":[0.000004020965,0.0000713118,0.0002167659,0.00008299252,0.00002447241,0.00007711349,0.000631189,0.9762931,0.000378211,0.001035261,0.00016734,0.02101825],"study_design_scores_gemma":[0.00006809659,0.00004723837,0.00001843661,0.0006370317,0.00001516707,0.00001111356,0.00009063652,0.9975359,0.0003693911,0.0003655563,0.0004893936,0.0003520023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2501827,0.005224663,0.7353802,0.0001167792,0.001917217,0.0007965981,0.000001219028,0.001355179,0.005025472],"genre_scores_gemma":[0.9893003,0.000152689,0.008829753,0.00009322565,0.0001735253,0.0001312668,0.000007482902,0.00002825795,0.001283484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7391176,"threshold_uncertainty_score":0.9999517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232312002582167,"score_gpt":0.269108499610323,"score_spread":0.2458772993521063,"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."}}