Bibliographic record
Abstract
For provisioning QoS guaranteed VPN services over packet-switching networks, the service controlling system must maintain the subscribed values of QoS parameters, especially the packet loss probability, to be below a preset number. Thus one of the main issues to be solved is to estimate the packet loss accurately and effectively based on the input stochastic traffic process. Inspired by the large deviation theory (LDT), two types of asymptotes loss estimator have been studied in the practical MPLS VPN networks: the large buffer asymptotic estimator (LBE) and the aggregate traffic approximation estimator (ATE). However, both estimators exhibit a large error from the actual loss ratio. A simple reactive estimator is proposed, which can adapt to the different contexts. The basic idea is to adjust the original estimator with one dynamic item that is based on the feedback of loss ratio measurement and adapt it to the changing of traffic model and buffer size. A series of experiments were devised to evaluate the performance of the new estimator under different traffic arrival models and different buffer sizes. The results show that the new practical estimator can calculate the loss probability more accurately.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".