Events in fiber optics given noisy OTDR data. I. GSR/MDL method
Bibliographic record
Abstract
This paper proposes a novel method of detecting and locating connection splice faults (events) in fiber optics by the digital signal processing (DSP) of noisy optical time-domain reflectometry (OTDR) data. This is motivated by the fact that as fiber becomes more widely adopted as a communications medium, methods of automated fault detection/location will become more important. The approach taken is to use Gabor series expansion coefficients to coarsely localize the faults. Due to the presence of measurement noise, these coefficients are random variables, and it is Gabor coefficients with a nonzero mean that determine fault presence and location. Coefficients with nonzero mean are found with the aid of Rissanen's minimum description length (MDL) criterion for model order estimation. The results show that the method is able to distinguish connection splice events from noise and the Rayleigh component in the OTDR data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.002 |
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".