Bibliographic record
Abstract
While there has been many studies on the efficient design of p-cycles focusing on optimizing their spare capacity efficiency, few of them consider such a design under the wavelength continuity assumption, i.e., no wavelength converter at any node. Consequently, few authors look at the routing and wavelength assignment in the context of p-cycles, where p-cycles have to be assigned the same wavelength as the paths of which they protect at least one link. In this paper, we propose to investigate thoroughly the issue of wavelength conversion vs. wavelength continuity for p-cycles, with large scale optimization tools (decomposition techniques) in order to get an exact estimate of the consequences of the wavelength continuity assumption on the spare capacity requirements and on the provisioning cost. The recourse to decomposition techniques allows the design of exact efficient scalable models contrarily to heuristics which ensure scalability but no accuracy guarantee. In particular, it allows an on-line generation of improving p-cycles, one after the other with respect to the objective, instead of a costly computing time off-line generation of p-cycles as in previous studies, a key issue for a scalable solution. Numerical results show that the difference between the capacity requirement under wavelength conversion vs. under wavelength continuity is meaningless. Consequently, in view of the reduced provisioning cost (saving at least on the converters), we advocate the design of p-cycles under a wavelength continuity assumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.001 |
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".