Long-Term Wavelength Stability of Large Type II FBG Arrays in Different Silica-Based Fibers at High Temperature
Bibliographic record
Abstract
Fiber Bragg gratings (FBGs) are useful components in fiber optic sensing systems, which can be highly multiplexed and distributed. In recent years, fabrication using ultrafast lasers has made these devices much more versatile and robust, but questions concerning their high-temperature performance remain. The wavelength resonance of an FBG is naturally sensitive to various parameters of its environment; in particular, changes in the temperature or strain of a fiber tend to induce observable shifts in the Bragg wavelength. Thus, FBGs can offer reliable sensing solutions, provided they are isolated from other influences and their wavelength responses remain well characterized. Nonetheless, it is important to be aware that the isothermal wavelength drift of unstrained FBGs has been previously observed. When this occurs, it can lead to measurement errors and a requirement for sensor recalibration. This study presents a comparison of long-term isothermal wavelength drifts observed at 600 °C, 800 °C, 900 °C and 1000 °C for large numbers of Type II FBGs in different kinds of single-mode fibers. The results provide guidance for the design of high-temperature sensing systems, both in terms of fiber selection and for estimating the maximum time before recalibration becomes necessary to maintain a specified accuracy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".