Bibliographic record
Abstract
A Quality of Service (QoS) routing architecture that balances Internet flow traffic with different QoS requirements in Agile All-Photonic Networks (AAPN) is presented. The architecture is based on the static and adaptive routing methods proposed previously for the AAPN network. The static routing method is based on the static Highest Random Weight (static HRW) used for designing load balanced web caches while the adaptive routing method is based on the adaptive Highest Random Weight (adaptive HRW) used for designing load balanced Internet routers. The architecture performance is investigated using traffic that belongs to two DiffServ traffic classes, namely: Expedite Forwarding (EF) that is sensitive to variations in end-to-end delay & traffic drop rate and Best Effort (BE) that can tolerate variations in end-to-end delay. Different routing methods are used to handle the two traffic classes: while the static routing method is used to route the EF traffic, the adaptive routing method is used route the BE traffic. The objective is to have EF packets that belong to the same flow traverse the same path and to preserve load balancing by remapping the BE flows when needed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".