Fault-localization comparative study of deploying monitoring trails to achieve survivability in all-optical networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, desirable performance of fault localization process in all-optical networks, by employing the recently introduced Monitoring-Trail (m-trail), is explored and a focus is given to the analysis on using m-trails with established lightpaths. A novel technique based on Geographic Midpoint Technique, an adapted version of the Chinese Postman's Problem (CPP) solution and an adapted version of the Traveling Salesman's Problem (TSP) solution algorithms is introduced. Examples are given to illustrate these techniques with a brief description on its establishment algorithms. A problem definition and an analysis of m-trail deployment to perform fault localization survivability in all-optical networks are presented. An optimized solution of the m-trail deployment procedure in all-optical networks that already established lightpaths is carried out by using FICO Xpress Mosel optimizer version 3.2.3.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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