{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001532658,0.0002568437,0.0002375358,0.0002016779,0.0003893257,0.0003475148,0.00160823,0.000174971,0.000004197967],"category_scores_gemma":[0.00001492133,0.000265335,0.0001118608,0.0001541923,0.00006228681,0.0005756385,0.001018244,0.0004995668,0.00004391824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002618991,"about_ca_system_score_gemma":0.0001729167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005142522,"about_ca_topic_score_gemma":0.0002493017,"domain_scores_codex":[0.9985463,0.0000935252,0.0001249851,0.0008903855,0.000103997,0.0002408079],"domain_scores_gemma":[0.9977763,0.00004144464,0.0003415865,0.001559779,0.0001852451,0.00009563805],"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":[0.00005489168,0.0001756947,0.007032833,0.0001658884,0.0001855551,0.0002053549,0.0005088196,0.9550964,0.0002077278,0.02995071,0.00008997344,0.006326152],"study_design_scores_gemma":[0.0003695652,0.00005987661,0.006194521,0.000200354,0.00007166275,0.000005445277,0.00003812955,0.9887792,0.00002294646,0.003829604,0.00005551821,0.0003732143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4504146,0.00006346127,0.548266,0.00002915712,0.0003787304,0.0001733691,0.000003899849,0.0001654321,0.0005053771],"genre_scores_gemma":[0.9976192,0.00008333319,0.0002467821,0.00002603043,0.00003548312,0.000002646938,0.00004165633,0.00001512699,0.001929714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5480192,"threshold_uncertainty_score":0.9999799,"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."}}