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Record W1907626704 · doi:10.1109/mascot.2000.876556

The synTraff suite of traffic modeling toolkits

2000· article· en· W1907626704 on OpenAlexafffund
Rama Balakrishnan, Carey Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMultifractal systemAutoregressive integrated moving averageComputer scienceWaveletFractalTraffic generation modelSuiteAutoregressive modelGaussianArtificial intelligenceReal-time computingMachine learningTime seriesMathematicsStatisticsGeography

Abstract

fetched live from OpenAlex

Describes three visually interactive tools for the analysis, modeling and generation of long-range dependent (LRD) network traffic: (1) the synTraff toolkit uses a three-step modeling approach based on F-ARIMA (fractal autoregressive integrated moving average) processes to generate monofractal traffic; (2) the WsynTraff toolkit implements the wavelet-domain independent Gaussian (WIG) model (S. Ma et al., 1998) for representing multifractal traffic; and (3) the MsynTraff toolkit models multifractal traffic using the multifractal wavelet model (MWM) (V. Ribeiro et al., 1999). All three tools can be used to generate synthetic LRD traffic streams for use in network simulations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.234
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2000
Admission routes2
Has abstractyes

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