Bibliographic record
Abstract
The use of deadline based channel scheduling in support of real time delivery of application data units (ADU's) is investigated. Of interest is priority scheduling where a packet with a smaller ratio of delivery deadline over number of hops to destination is given a higher priority. It has been shown that a variant of this scheduling algorithm, based on head-of-the-line priority, is efficient and effective in supporting real time delivery of ADU's. In this variant, packets with a ratio smaller than or equal to a given threshold are sent to the higher priority queue. We first present a technique to select this threshold dynamically. The effectiveness of our technique is evaluated by simulation. We then study the performance of deadline based channel scheduling for large networks, with multiple autonomous systems. For this case, accurate information on number of hops to destination may not be available. A technique to estimate this distance metric is presented. The effectiveness of our algorithm with this estimated distance metric is evaluated. In addition, we study the performance of a multi-service scenario where only a fraction of the routers deploy deadline based channel scheduling.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".