On multicast traffic grooming in WDM networks
Why this work is in the frame
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Bibliographic record
Abstract
We investigate the problem of grooming dynamic multicast traffic in WDM mesh networks. This problem is equivalent to designing a light-tree based logical topology for multicast streams. It consists of four subproblems, namely routing, wavelength assignment, design of a light-tree based logical topology, and traffic-grooming. We develop different routing schemes to efficiently groom low-speed connections on the light-tree based logical topology. Numerical results demonstrate that the proposed approaches use the network resources more efficiently compared to the nongrooming approach and the approach of serving the multicast requests as separate unicast requests. Moreover, amongst the proposed techniques, the logical-first multihop grooming scheme MC-MHl outperforms all other schemes in terms of blocking probability and performance gain.
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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 it