Importance of Optical Metrics for IGP Configuration Change Prediction
Bibliographic record
Abstract
We used Machine Learning (ML) algorithms on data provided by a client’s real network to identify the role of optical device metrics in detecting flapping links (links that go down multiple times a day) and Interior Gateway Protocol (IGP) configuration changes in the next five days based on data collected for the previous five days. Our prototype shows that we could predict upcoming IGP changes five days ahead with 95% precision and 75% recall. Adding optical data to the ML model increased the performance by about 9%. The importance of optical features like “OCH-OPTMAX”, “OCH-OPTMIN”, “OCH-DGDMAX”, “E-SES”, “E-UAS” and “E-ES” that account for optical minimum power, unavailable seconds, and errored seconds is confirmed using common feature importance wrapper methods. This ML approach is significantly better than existing manual methods followed by the operator.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".