{"id":"W2049024242","doi":"10.1016/j.automatica.2014.05.004","title":"Markovian jump guaranteed cost congestion control strategies for large scale mobile networks with differentiated services traffic","year":2014,"lang":"en","type":"article","venue":"Automatica","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Network congestion; Computer science; Markov process; Quality of service; Network traffic control; Jump; Mathematical optimization; Control (management); Computer network; Distributed computing; Control theory (sociology); 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":[0.001845756,0.001080199,0.001200178,0.0005600321,0.0006375466,0.001301862,0.001696436,0.0009716118,0.00218556],"category_scores_gemma":[0.004519631,0.0004700159,0.0004745382,0.0006710162,0.001187846,0.001103371,0.001434622,0.001379609,0.0001498401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002000089,"about_ca_system_score_gemma":0.001583354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01015466,"about_ca_topic_score_gemma":0.008194244,"domain_scores_codex":[0.9991559,0.000226038,0.00003568836,0.0001376012,0.0001985151,0.0002462344],"domain_scores_gemma":[0.9974355,0.001630553,0.0002566077,0.0001140068,0.0004281074,0.0001352694],"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":[0.0002376647,0.00008001643,0.0002268163,0.00007057124,0.00004488198,0.00006259621,0.00007180868,0.960496,0.002116147,0.01860313,0.001052488,0.01693793],"study_design_scores_gemma":[0.00001512899,0.00002597955,0.00004835904,0.000002150602,0.000008230902,0.00000470864,0.000004409408,0.9968958,0.0001286207,0.002801592,0.00006128531,0.000003650222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08706404,0.0005289359,0.9075093,0.0004939967,0.0001403606,0.00008275062,0.00007433637,0.0003182048,0.003788023],"genre_scores_gemma":[0.9910884,0.00010031,0.007213742,0.00006193903,0.00003228535,0.00003421929,0.0000191315,0.00001967246,0.001430362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01015466,"threshold_uncertainty_score":0.02019113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003548049362620289,"score_gpt":0.2044259144615204,"score_spread":0.2008778650989002,"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."}}