Differential Brillouin gain for improving the temperature accuracy and spatial resolution in a long-distance distributed fiber sensor
Bibliographic record
Abstract
We demonstrate a 12 km differential pulse-width pair Brillouin optical time-domain analysis (DPP-BOTDA) using 40 ns and 50 ns pulses with DC-coupled detection. A spatial resolution of 1 m and a narrowband Brillouin gain spectrum of 33 MHz are obtained simultaneously compared with 88 MHz with the use of 10 ns pulses in a conventional BOTDA. The experimental results show that the differential Brillouin gain of a 40/50 ns pulse pair is 7 times stronger than the direct Brillouin gain of BOTDA with the use of a 10 ns pulse, and the temperature uncertainty is 0.25 degrees C compared with 1.8 degrees C for a 10 ns pulse. As the pulse-width difference decreases from 10 ns to 1 ns, corresponding to a spatial resolution from 1 m to 10 cm, the prediction of temperature uncertainty will only increase from 0.25 degrees C to 0.8 degrees C for DPP-BOTDA over a 12 km long single-mode fiber.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".