{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000684716,0.0005703505,0.0008079103,0.0005045129,0.0004347836,0.0007561092,0.0009523905,0.0007020587,0.0006806137],"category_scores_gemma":[0.002495749,0.000354702,0.0004017077,0.0008183682,0.000509697,0.001220634,0.0006472015,0.0008286554,0.0001761041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009727404,"about_ca_system_score_gemma":0.0009498644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005016195,"about_ca_topic_score_gemma":0.005185377,"domain_scores_codex":[0.9997086,0.0000780922,0.00001592172,0.00005666692,0.00007457787,0.00006606478],"domain_scores_gemma":[0.9989136,0.0006483323,0.0001578949,0.00004990508,0.000192697,0.00003759647],"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":[0.000038847,0.00004838116,0.0006875937,0.00003325512,0.00001399941,0.00004696643,0.00003319124,0.9513739,0.001782789,0.003259313,0.0004158998,0.04226588],"study_design_scores_gemma":[0.000001482672,0.00001359033,0.00004952265,0.000002376863,0.000001976019,0.000006342927,0.000006455889,0.9983353,0.0003511927,0.001138839,0.00009141313,0.000001601563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04986278,0.0005666995,0.9472533,0.0002399362,0.00003034725,0.00003965199,0.00003117264,0.0002682956,0.001707846],"genre_scores_gemma":[0.8075756,0.0005934531,0.1885817,0.000148826,0.00006013755,0.0000905992,0.00008600877,0.00005129508,0.002812377],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005016195,"threshold_uncertainty_score":0.009974003,"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."}}