Routing and wavelength assignment for permanent and reliable wavelength paths in WDM networks
Bibliographic record
Abstract
We tackle the routing and wavelength assignment problem for wavelength division multiplexing (WDM) networks containing permanent and reliable wavelength paths (WPs). It consists of finding the routes and the wavelength assignment for the normal state of the network and for the important failure scenarios. These scenarios might be the most probable failure scenarios or simply the failure scenarios of interest to the network planner (e.g., the single link failure scenarios). We propose an integer mathematical programming model for this problem. This model supposes that the routing is based on a weighted shortest path policy. From the implementation simplicity and the network performance standpoint, this type of routing is the best one. In order to find solutions, a greedy heuristic is proposed. This heuristic first finds the routing of the permanent and reliable WPs in the normal state of the network and assign heuristically wavelength to WPs. Next, the failure scenarios are treated separately. Finally, a detailed example is presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".