{"id":"W2291009813","doi":"10.1109/glocom.2015.7417804","title":"Towards Intelligent LTE Mobility Management through MME Pooling","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Pooling; Mobility management; Quality of service; Distributed computing; Overhead (engineering); Heuristic; Context (archaeology); Computer network; Mobility model; Latency (audio); Mobile computing; Mathematical optimization; Artificial intelligence; Mathematics","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":[0.001583047,0.0008204608,0.000822905,0.0005351242,0.0007367047,0.001635087,0.001643654,0.000901848,0.0008104051],"category_scores_gemma":[0.002356439,0.0003160793,0.000503957,0.0007808838,0.0006185259,0.00310175,0.002531442,0.0007870706,0.0002731403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009680598,"about_ca_system_score_gemma":0.0009999969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002130353,"about_ca_topic_score_gemma":0.001976219,"domain_scores_codex":[0.9990459,0.0003479302,0.00004642344,0.0001667168,0.0001743507,0.0002186568],"domain_scores_gemma":[0.9991247,0.0003110975,0.0001710259,0.0001671157,0.0001304289,0.00009558949],"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":[0.0004138969,0.0002709488,0.001854558,0.0001638196,0.000117596,0.0002666168,0.0003631913,0.6515775,0.02723587,0.05037551,0.00407624,0.2632842],"study_design_scores_gemma":[0.00002156115,0.00009281083,0.0002243687,0.000008259525,0.00002404685,0.00005687014,0.00008338079,0.9836192,0.00299079,0.01114103,0.001722419,0.00001532358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0401157,0.0007069982,0.9559681,0.000411791,0.00004040048,0.0000722743,0.00002952297,0.0002792249,0.002375976],"genre_scores_gemma":[0.8825824,0.0003434207,0.1154523,0.0001782972,0.00007675829,0.00007321993,0.00005778728,0.00002225532,0.001213478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002130353,"threshold_uncertainty_score":0.008372009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.198295395490307,"score_gpt":0.4005172549099296,"score_spread":0.2022218594196226,"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."}}