Towards tuning of thermal sensitivity of the long period fiber gratings using a liquid crystal layer
Bibliographic record
Abstract
Towards tuning of thermal sensitivity of the long period fiber gratings using a liquid crystal layer A high-efficiency thermal tuning filter based on a long-period fiber grating (LPFG) combined with a low-birefringence liquid crystal (LB LC) cladding layer is presented. Two types of LPFGs were studied and compared: the LPFGs based on a standard telecommunication fiber produced by an electric arc technique, and the LPFGs based on a boron co-doped fiber written by a UV technique. Both types of LPFGs when enhanced with an external LB LC layer exhibit two different temperature sensitivities, which depend on the temperature range of operation. For the LPFGs based on standard telecommunication fiber we can conclude that the presence of the LB LC cladding increases the thermal tuning efficiency by more than one order of magnitude over the value for the LPFGs in air. In the case of the LPFGs based on the boron co-doped fiber we discovered it is possible to obtain either a temperature-independent attenuation band or the attenuation bands with high temperature sensitivities, just by careful choice of the order of the cladding mode and the operating wavelength.
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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".