{"id":"W2912078769","doi":"10.1109/iwqos.2018.8624176","title":"Toward Smart and Cooperative Edge Caching for 5G Networks: A Deep Learning Based Approach","year":2018,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Backhaul (telecommunications); Base station; Computer network; Cache; Mobile edge computing; Edge device; Edge computing; Software deployment; Cellular network; Mobile broadband; Bandwidth (computing); Latency (audio); The Internet; Broadband; Enhanced Data Rates for GSM Evolution; Server; Telecommunications; Wireless; Cloud computing; 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":[0.0005259436,0.0008473788,0.0009537303,0.0004939368,0.0004737565,0.0009059487,0.001868303,0.001312878,0.0009242098],"category_scores_gemma":[0.001317875,0.000307134,0.0004581017,0.0007951963,0.00058772,0.001442905,0.0009106286,0.001235558,0.0002734178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075306,"about_ca_system_score_gemma":0.001459985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01737241,"about_ca_topic_score_gemma":0.02344133,"domain_scores_codex":[0.9997403,0.00005024967,0.00001177397,0.00006491377,0.00005635661,0.0000763619],"domain_scores_gemma":[0.9995773,0.0001567114,0.00004269888,0.00005022098,0.0001252143,0.00004781277],"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":[0.0002175151,0.0003377446,0.003995639,0.000120011,0.0001461918,0.0001171483,0.0001365308,0.7556137,0.006063075,0.01067273,0.007316849,0.2152628],"study_design_scores_gemma":[0.000004631656,0.0000225103,0.0001086795,0.000004138057,0.000008265219,0.000008013996,0.00001251755,0.9969345,0.0004378624,0.002124037,0.0003315988,0.000003231956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1330445,0.004170988,0.8518344,0.00192573,0.0001427551,0.00008579145,0.0003364618,0.001799908,0.006659514],"genre_scores_gemma":[0.902665,0.0008475769,0.09088202,0.0006004995,0.00009371613,0.00006846749,0.0005203323,0.00006188272,0.004260411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01737241,"threshold_uncertainty_score":0.03454256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02977448242127174,"score_gpt":0.2343641885229783,"score_spread":0.2045897061017066,"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."}}