Novel techniques for deploying monitoring trails (m-trails) for fault localization in all-optical networks
Bibliographic record
Abstract
In this paper, desirable performance of fault localization process in all-optical networks is presented by employing the recently introduced Monitoring-Trail (m-trail) (that was proved to yield better performance by establishing monitoring resources in a shape of trails). As well, new techniques for deploying m-trails on networks along with its established lightpaths to perform fault localization are introduced. A novel technique based on Geographic Midpoint and the use of pair-wise shortest-paths that employ the standard Dijkstra algorithm, an adapted Chinese Postman's Problem (CPP) solution and adapted Traveling Salesman's Problem (TSP) solution algorithms. In addition, a manual exercise method can be directly applied to the ACT table. Different examples are given to illustrate these techniques with a brief description on its establishment algorithms. Using m-trails with established lightpaths to perform fault localization is a superb technique as it saves network resources; by reducing the number of the m-trails required for fault localization and hence the number of wavelengths used in the network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".