{"id":"W2053761086","doi":"10.1002/dac.1262","title":"UARA in edge routers: an effective approach to user fairness and traffic shaping","year":2011,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University; Polytechnique Montréal","keywords":"Computer science; Computer network; Provisioning; Network congestion; Quality of experience; Enhanced Data Rates for GSM Evolution; Quality of service; Traffic shaping; The Internet; Bandwidth (computing); Network traffic control; Peer-to-peer; Traffic congestion; Network packet; Telecommunications; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003717307,0.0006514032,0.001021601,0.0008106155,0.001338929,0.002177008,0.002365074,0.001412811,0.002147251],"category_scores_gemma":[0.006376331,0.0003392955,0.0004480879,0.0005365213,0.001220638,0.002060253,0.002598338,0.001702863,0.0005721574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005912,"about_ca_system_score_gemma":0.001132177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009070475,"about_ca_topic_score_gemma":0.001022584,"domain_scores_codex":[0.9967775,0.001723943,0.0001156613,0.0004531757,0.0005153595,0.0004145087],"domain_scores_gemma":[0.9938942,0.002471938,0.0004039161,0.001491641,0.001299496,0.0004388809],"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":[0.001284813,0.001006472,0.004451065,0.0001395719,0.000141712,0.000658947,0.0008393266,0.3475305,0.07371771,0.2575244,0.005598796,0.3071067],"study_design_scores_gemma":[0.00002090637,0.0001236165,0.0001991461,0.000008211757,0.00002218223,0.000109724,0.00004351503,0.9713867,0.007903324,0.01735723,0.002804197,0.00002122922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04158346,0.0003096358,0.9512815,0.0002660127,0.00008908263,0.0001036628,0.00001290879,0.0007280582,0.005625676],"genre_scores_gemma":[0.8523455,0.00010688,0.1440597,0.0002876416,0.0001097864,0.00007664852,0.0000148405,0.00006442952,0.002934591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003717307,"threshold_uncertainty_score":0.01965922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06499478482198469,"score_gpt":0.2824103312369798,"score_spread":0.2174155464149952,"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."}}