Supervised and unsupervised learning for classifying changes in optical time domain reflectometer traces
Bibliographic record
Abstract
Global telecommunications heavily rely on optical fibers as the foundation of their network infrastructure, making it imperative for network operators to ensure their dependability. The traditional optical time domain reflectometer (OTDR) focuses on event detection, but in-service measurements can detect the interactions of distributed effects such as fiber loss, Raman amplification, stimulated Raman scattering, and channel loading. This research paper demonstrates the effectiveness of supervised and unsupervised learning models in accurately categorizing changes observed in in-service OTDR traces. Among the supervised models tested, the multilayer perceptron exhibited superior performance with a classification accuracy of 0.891 on multiple-effect data, surpassing the random forest and convolutional neural network. Clustering models were also explored, focusing on single-effect data; the best result was obtained using the Gaussian mixture model, achieving a normalized mutual information of 0.663 and an adjusted Rand index of 0.52.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".