{"id":"W1480850188","doi":"10.1109/ccece.2015.7129477","title":"Performance optimization of big data in mobile networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Bandwidth (computing); Cache; Service provider; Computer network; Mobile telephony; Transfer (computing); Big data; Data as a service; Data transmission; Service (business); Mobile radio; Data mining","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":[0.001243033,0.0007446039,0.0006196434,0.0006517153,0.00063447,0.0009089888,0.0007595312,0.0005344771,0.0006867697],"category_scores_gemma":[0.003392296,0.0002171511,0.0002047299,0.001041035,0.0005880037,0.001291682,0.0007101369,0.0005100471,0.0001228885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232703,"about_ca_system_score_gemma":0.0007061528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002920462,"about_ca_topic_score_gemma":0.002744471,"domain_scores_codex":[0.9993126,0.0002447913,0.00003047074,0.0000835704,0.0002091729,0.0001193303],"domain_scores_gemma":[0.9982216,0.001194753,0.0001289579,0.0001110587,0.000252587,0.00009101373],"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":[0.0002095688,0.00007608275,0.001130503,0.00006097838,0.00002667958,0.00004687094,0.00004815358,0.9430551,0.004266687,0.004276746,0.001252185,0.04555049],"study_design_scores_gemma":[0.000004250326,0.00002895723,0.0001464159,0.000001465655,0.000002481856,0.00001019291,0.00001473418,0.9973904,0.001118632,0.001141378,0.0001391071,0.000002012021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3521493,0.003477096,0.6333483,0.001328818,0.0001602175,0.0001169454,0.0002034357,0.001098112,0.008117716],"genre_scores_gemma":[0.9657291,0.000313023,0.0329759,0.00005827888,0.00003843808,0.0000340978,0.00007326929,0.00004118938,0.0007366927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002920462,"threshold_uncertainty_score":0.008943915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06008354313924276,"score_gpt":0.2659210519668033,"score_spread":0.2058375088275606,"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."}}