{"id":"W2964421893","doi":"10.1109/tnse.2019.2932727","title":"Repairable Fountain Coded Storage Systems for Multi-Tier Mobile Edge Caching Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Cache; Computer network; Cloud computing; Exploit; Upload; Locality; Enhanced Data Rates for GSM Evolution; Base station; Mobile edge computing; Distributed computing; Server; Operating system; Computer security; Telecommunications","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":[0.0006038852,0.0005075297,0.000698427,0.0005620428,0.0009056843,0.0008624882,0.001610692,0.0007012155,0.001375635],"category_scores_gemma":[0.002391931,0.000153879,0.0002313747,0.0006783475,0.0006384614,0.001374734,0.0009978713,0.000544414,0.000274734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001690438,"about_ca_system_score_gemma":0.00101692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007000702,"about_ca_topic_score_gemma":0.008309532,"domain_scores_codex":[0.9995757,0.0001025482,0.00002887884,0.00006478855,0.0001279494,0.0001001803],"domain_scores_gemma":[0.9984883,0.0004390014,0.0001994701,0.0002885718,0.000494933,0.00008972611],"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":[0.001109981,0.0001899133,0.002711056,0.0003041296,0.0001059908,0.0008702585,0.0005064126,0.6740475,0.04219955,0.06802203,0.008106134,0.201827],"study_design_scores_gemma":[0.00001111156,0.00007369941,0.0001340515,0.00001140063,0.00001309499,0.0001301181,0.00003335482,0.9902626,0.004445807,0.003694931,0.001168601,0.00002113185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1407259,0.002468813,0.8509367,0.0003111187,0.0001514053,0.0001071101,0.000104455,0.001598977,0.003595484],"genre_scores_gemma":[0.9600273,0.0002538692,0.03800726,0.00008377172,0.00002437224,0.00004183055,0.00004763959,0.00002131542,0.001492654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007000702,"threshold_uncertainty_score":0.01391995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143285719369687,"score_gpt":0.2176523186256862,"score_spread":0.2033237466887174,"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."}}