{"id":"W2756113194","doi":"10.48550/arxiv.1709.05377","title":"Dynamic Mobile Edge Caching with Location Differentiation","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; Ministry of Education, India; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Backhaul (telecommunications); Exploit; Popularity; Content delivery; Cache; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Latency (audio); Computer network; Real-time computing; Base station; Artificial intelligence; Computer security; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005793256,0.0005986622,0.0008836472,0.0004487481,0.0004631756,0.0008181913,0.001541722,0.0008498641,0.0006134947],"category_scores_gemma":[0.002524705,0.0002822404,0.0003015757,0.0008573245,0.0005071377,0.001166376,0.0009197372,0.0006519243,0.0003284455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009446591,"about_ca_system_score_gemma":0.0008541853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005422171,"about_ca_topic_score_gemma":0.006891188,"domain_scores_codex":[0.9994898,0.0001225285,0.00002504013,0.000151173,0.0001018925,0.0001096355],"domain_scores_gemma":[0.9990091,0.0003483807,0.0001375773,0.0002336716,0.0002017649,0.00006951271],"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":[0.0003511592,0.0001783254,0.005839373,0.00007414402,0.00006327465,0.0003348475,0.0001381602,0.8094444,0.01238243,0.01565774,0.002877088,0.1526591],"study_design_scores_gemma":[0.00000763266,0.00002804841,0.0002432703,0.000002650845,0.000008029677,0.00006992699,0.000009132833,0.9964333,0.001268158,0.001543951,0.0003795603,0.000006402237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1198672,0.0007845568,0.8742634,0.000267595,0.00006234791,0.00007605429,0.0001034604,0.001102434,0.003472896],"genre_scores_gemma":[0.9699859,0.0001123515,0.02850908,0.00006656405,0.00002464099,0.0000205552,0.00005016513,0.00001529778,0.001215572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005422171,"threshold_uncertainty_score":0.01078123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03916561685917943,"score_gpt":0.1786717447753173,"score_spread":0.1395061279161379,"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."}}