{"id":"W2096481382","doi":"10.1109/vetecs.2005.1543711","title":"Estimating Heavy-tails in Long-range Dependent Wireless Traffic","year":2005,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Heavy-tailed distribution; Estimator; Range (aeronautics); Marginal distribution; Estimation theory; Computer science; Extreme value theory; Tail dependence; Wireless; Statistics; Algorithm; Probability distribution; Statistical physics; Mathematics; Random variable; Physics; Engineering; Multivariate statistics; Telecommunications","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":[0.00179462,0.0005210926,0.0004841997,0.001011428,0.0002738363,0.0006058979,0.000785682,0.0008265848,0.0002570564],"category_scores_gemma":[0.01479287,0.0004568232,0.0002679737,0.0007371405,0.000647994,0.00135047,0.0008562974,0.001059108,0.0001305101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000414085,"about_ca_system_score_gemma":0.0003354205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251489,"about_ca_topic_score_gemma":0.0009524444,"domain_scores_codex":[0.9993427,0.0002612956,0.00003745141,0.00009890779,0.0002032854,0.0000564008],"domain_scores_gemma":[0.9914829,0.005953763,0.0009849089,0.0006914407,0.0007495965,0.0001374334],"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":[0.0004220097,0.0002697887,0.06997823,0.0001165552,0.0001453004,0.0003735764,0.0003542865,0.7420186,0.02968085,0.01065842,0.0008113106,0.1451712],"study_design_scores_gemma":[0.000006587879,0.00003350831,0.005467977,0.000005215295,0.000005540793,0.00008207749,0.00003086339,0.9854299,0.003550928,0.005252112,0.0001192318,0.0000160656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4576577,0.00009192829,0.541375,0.00008787811,0.00001131941,0.00001807545,0.00005974397,0.0003108643,0.0003874299],"genre_scores_gemma":[0.954985,0.00008033001,0.04442674,0.0000298685,0.00001849095,0.00002258887,0.0001997197,0.00002948558,0.0002079201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00179462,"threshold_uncertainty_score":0.009490967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009843543576819564,"score_gpt":0.2323943656473216,"score_spread":0.222550822070502,"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."}}