Low-Loss Random Fiber Gratings Made With an fs-IR Laser for Distributed Fiber Sensing
Bibliographic record
Abstract
By using the plane-by-plane grating inscription method with an fs-IR laser, random fiber gratings with low laser-induced loss were fabricated in SMF-28 fiber for distributed temperature sensing. Compared to conventional random gratings having broadband backscattering enhancement, the demonstrated random fiber grating has a well defined narrower bandwidth of backscattering enhancement, higher laser-induced backscattering level, and lower laser-induced loss, which are critical for long-haul distributed fiber sensor systems with high measurement accuracy. Experimental results showed that by using fabricated random fiber gratings along with the technique of optical frequency-domain reflectometry, a distributed temperature fiber sensor could be realized having a gauge length of 10 mm, standard deviation of temperature measurement as low as 0.00085 °C, and laser-induced loss of 0.08 dB/m.
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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) | 0.001 | 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 | 0.001 |
| 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".