{"id":"W2139079053","doi":"10.1007/s11235-009-9201-x","title":"A new structure-preserving method of sampling for predicting self-similar traffic","year":2009,"lang":"en","type":"article","venue":"Telecommunication Systems","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Autoregressive integrated moving average; Sampling (signal processing); Internet traffic; Data mining; Self-similarity; Systematic sampling; Stratified sampling; Bandwidth (computing); Traffic generation model; Real-time computing; The Internet; Time series; Statistics; Machine learning; Computer network; Telecommunications; Detector; 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":[0.00154269,0.0005451282,0.00113035,0.0006973323,0.0005309023,0.0006954571,0.00195493,0.001003503,0.0009664568],"category_scores_gemma":[0.004824824,0.0005412421,0.0007738826,0.0007329949,0.0007121124,0.001292894,0.0009025515,0.001222402,0.0002583308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005280602,"about_ca_system_score_gemma":0.0009896038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004687528,"about_ca_topic_score_gemma":0.004368859,"domain_scores_codex":[0.9994882,0.0001923258,0.00002208929,0.00008889851,0.0001696587,0.00003881926],"domain_scores_gemma":[0.9979907,0.001152257,0.0001142328,0.0002725308,0.0003656062,0.0001046818],"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":[0.0003144284,0.0002450167,0.003637054,0.0001047042,0.0001391288,0.00009596453,0.000106117,0.7379656,0.01492285,0.0201177,0.001665781,0.2206857],"study_design_scores_gemma":[0.000003805754,0.00001158761,0.00006169481,8.045461e-7,0.000002848506,0.000005907659,9.961229e-7,0.998822,0.0003943109,0.0006030288,0.00009036805,0.000002613924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01164701,0.00008288542,0.9877796,0.00003250914,0.00003258307,0.00002457811,0.00002924641,0.000196844,0.0001749076],"genre_scores_gemma":[0.3297679,0.0002300064,0.6674326,0.000123206,0.000165119,0.0002060345,0.0002880793,0.0001244119,0.001662598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004687528,"threshold_uncertainty_score":0.009320438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02779606981809545,"score_gpt":0.29910364441144,"score_spread":0.2713075745933445,"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."}}