Simultaneous strain and temperature measurements with polarization-maintaining fibers and their error analysis by use of a distributed Brillouin loss system
Bibliographic record
Abstract
Simultaneous temperature and strain measurement with a distributed Brillouin loss system is proposed by use of the parameters Brillouin frequency, power, and bandwidth, for PANDA, bow-tie, and tiger polarization-maintaining fibers for the first time to our knowledge. The expressions for simultaneous temperature and strain sensing and the maximum errors and rms values of temperature and strain measurements are derived with three combinations of the parameters: (1) power and Brillouin frequency, (2) bandwidth and Brillouin frequency, and (3) bandwidth and Brillouin power. Our experiments demonstrate that simultaneous temperature and strain sensing at 20-cm spatial resolution for Brillouin frequency combined with bandwidth the strain/temperature resolutions are 39 microepsilon/2 degrees C (PANDA), 126 microepsilon/3 degrees C (bow tie), and 598 microepsilon/16 degrees C (tiger); for the Brillouin frequency combined with power the strain/temperature resolutions are 153 microepsilon/8 degrees C (PANDA) and 237 microepsilon/4 degrees C (bow tie); and for the bandwidth combined with power the strain/temperature resolutions are 135 microepsilon/38 degrees C (PANDA) and 195 microepsilon/38 degrees C (bow tie).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".