{"id":"W2099890714","doi":"10.1109/tnet.2005.861253","title":"An estimator of regulator parameters in a stochastic setting","year":2005,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique; University of Toronto","funders":"","keywords":"Burstiness; Computer science; Estimator; Queue; Queueing theory; Provisioning; Mathematical optimization; Algorithm; Computer network; Statistics; Network packet; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000406136,0.0002066126,0.0002760463,0.0002350711,0.0001639629,0.00007990675,0.0007789158,0.0001052549,0.000009387421],"category_scores_gemma":[0.000009579059,0.0002196522,0.0001058741,0.0006320121,0.00005602999,0.0004403405,0.000005813687,0.0003213756,0.00001299183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008663977,"about_ca_system_score_gemma":0.00007724101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001423734,"about_ca_topic_score_gemma":0.00009706567,"domain_scores_codex":[0.9982233,0.0001281278,0.0004659189,0.0004629146,0.0002841632,0.000435548],"domain_scores_gemma":[0.9984531,0.0003969345,0.0001483345,0.0008130768,0.00004763125,0.0001409293],"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":[0.00001702407,0.00007898828,0.00002216302,0.000002475747,0.00001148255,0.000002516015,0.0001742846,0.5716693,0.00007330946,0.0002651749,0.00001585438,0.4276675],"study_design_scores_gemma":[0.0006480319,0.0001153433,0.0001631027,0.000170146,0.00001779225,0.0000149692,0.00002995672,0.997631,0.0003512265,0.0004233449,0.0002110097,0.0002241005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06746405,0.0001178803,0.9304783,0.0006826418,0.0007655247,0.0002283544,0.000001892616,0.0002148382,0.00004655263],"genre_scores_gemma":[0.9103473,0.000006916364,0.08913479,0.0002214457,0.0002033628,0.00004340737,7.883506e-7,0.00001873844,0.0000233002],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8428832,"threshold_uncertainty_score":0.8957158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470193086311258,"score_gpt":0.2514101569266887,"score_spread":0.2367082260635761,"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."}}