Detection and Location of Connection Splice Events in Fiber Optics Given Noisy OTDR Data—Part II: R1MSDE Method
Bibliographic record
Abstract
In Part I (Liu and Zarowski 2001), a method of connection splice event detection and location by the digital signal processing of noisy optical time-domain reflectometry data was given. It applied the Gabor series representation (GSR) for transient signal detection of Friedlander and Porat (1989) to the optical time-domain reflectometer (OTDR) data yielding Gabor coefficients. The Rissanen minimum description length (MDL) criterion was then applied to the Gabor coefficients to eliminate those that are most likely due to noise alone. The surviving coefficients are indexed in such a manner as to indicate the location of events in the OTDR data. Although this GSR/MDL method is computationally efficient, it only gives a coarse estimate of event location. Therefore, this paper (Part II) develops the rank-1 matched subspace detection and estimation algorithm that employs more computation to achieve a greater accuracy in event position estimation. It is based on the matched subspace detection theory of Scharf and Friedlander.
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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 | 0.000 |
| 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".