Prioritized e‐mail servicing to reduce non‐spam delay and loss: A performance analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper proposes a prioritized e‐mail servicing on e‐mail servers to reduce the delay and loss of non‐spam e‐mails due to queuing. Using a prioritized two‐queue scheme, non‐spam e‐mails are queued in a fast queue and given higher service priority than spam e‐mails that are queued in a slow queue. Four prioritized e‐mail service strategies for the two‐queue scheme are proposed and analyzed. We modeled these four strategies using discrete‐time Markov chain analysis under different e‐mail traffic loads and service capacities. Non‐spam e‐mails can be delivered within a small delay, even under heavy e‐mail loadings and high spam‐to‐non‐spam a priori. Results from our analysis of the two‐queue scheme show that it gives non‐spam delay and loss probability two orders of magnitude smaller than the typical single‐queue approach during heavy spam traffic. Moreover, prioritized e‐mail servicing protects e‐mail servers from spam attacks. Copyright © 2007 John Wiley & Sons, Ltd.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it