Monitoring local temperature and longitudinal strain along a nonuniform As<sub>2</sub>Se<sub>3</sub>-PMMA tapered fiber by Brillouin gain-profile tracing
Bibliographic record
Abstract
The local temperature and longitudinal strain at spatial resolution of 0.5% of the pulse-width equivalent length along a nonuniform As2Se3-PMMA tapered fiber is investigated by a Brillouin gain-profile tracing method. This scheme uses a 20 ns pump pulse with the pulse-width equivalent length longer than the fiber under test (FUT) of 50 cm nonuniform As2Se3-PMMA tapered fiber. The whole interaction process of long pump pulse is investigated including pump pulse entering the FUT, overlapping completely with FUT and leaving the FUT. The evolution of Brillouin gain spectrum (BGS) along the nonuniform fiber is formed by the subtraction of frequency-domain BGS of two adjacent sensing points in the trailing edge (where the pump pulse leaves the FUT) of the BOTDA spectrum. The trailing part is preferred due to the pre-amplified acoustic field by the long pumping pulse. Then the local responses of temperature and wide-range longitudinal strain with high spatial resolution of 1.1 cm along the nonuniform As2Se3-PMMA tapered fibers are investigated. The change of the local temperature and strain shifts the BGS that is different along the nonuniform fibers, which forms the distributed measurement. The spatial resolution, the fiber length of the detected section in the proposed method, is 1.1 cm for the local temperature and longitudinal strain measurement, which is 0.5% of the pulse-width equivalent length and is limited by the sampling rate of data acquisition and the fall-time of the pump pulse.
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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 | 0.000 |
| 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 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".