Accurate compensation and prediction of the temperature cross-sensitivity of tilted FBG cladding mode resonances
Bibliographic record
Abstract
The temperature dependence of core mode resonance has been thoroughly studied in fiber Bragg gratings (FBGs), but it is not the case for cladding mode resonances in multi-resonance gratings such as tilted FBGs (TFBGs). In this work, the temperature sensitivity of ultraviolet written TFBGs in SMF-28 fibers is assessed, demonstrating in the first, to the best of our knowledge, place that a single gauge factor K T = 6.25 ⋅ 10 − 6 ± 0.02 ⋅ 10 − 6 ∘ C − 1 can be employed to characterize the response to temperature of the resonances over the full spectrum in the 10°–50°C range. Then, a simulation model is obtained, enabling to predict TFBG spectra in the 10°–50°C range with high accuracy. This requires a calibration of the core index and dispersion of the TFBG measured in air at 25°C, and determination of the glass refractive index thermo-optic coefficient ( d n / d T = 8.46 ⋅ 10 − 6 ± 0.1 ⋅ 10 − 6 ∘ C − 1 , common to both core and cladding glasses), leading to a mean error on the wavelength position of resonances between 1 and 3 pm. This mean error can be further reduced (less than 1 pm) by considering a linear dependence with temperature of d n / d T . Therefore, this model will enable to completely remove the temperature-induced shifts of all resonances in TFBG sensing applications and measure with great accuracy the variables of interest by using the scaled averages of groups of resonances instead of (less accurate) individual shifts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".