Temperature Sensing of Stepped-Metal Coated Optical Fiber Bragg Grating with the Restructured Dual-Peak Resonance
Bibliographic record
Abstract
This paper demonstrates a method for the development of stepped-metal coating on optical fiber Bragg grating. The paper also analyzes the dual-peak resonance restructured by the nickel/copper stepped-metal coating. According to the coefficients of linear thermal expansion of the coatings, the modeling and experimental analysis is categorized into three types: Type A, Type B, and Type C. We denote the temperature sensitivity difference between the two peaks as ΔKT for Type A, ΔK′T for Type B, and ΔK″T for Type C. The experimental results show that ΔKT, ΔK′T, and ΔK″T are 2.1 pm/°C, 6.5 pm/°C, and 0.8 pm/°C, respectively. The model analysis and the experimental results all show the Type B stepped-metal coating causes the most obvious temperature sensitivity difference between the two resonance peaks. The stepped-metal coating on the same one Bragg grating can restructure the single resonance into dual-peak resonance with different temperature sensing, and Type B can be used to develop a dual-parameter optical fiber Bragg grating sensor at one location which can measure two physical parameters simultaneously.
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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 | 0.001 | 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".