Packaging and precision testing of fiber-Bragg-grating and silicon ring-resonator thermometers: current status and challenges
Bibliographic record
Abstract
Abstract In recent years photonic thermometers—temperature sensors based on optical frequency measurement which exploit the thermo-optic effect to translate thermal changes into frequency shifts—are gaining popularity as a possible alternative to their electrical counterparts: platinum resistance thermometers and thermocouples. In this work, we report our results of testing photonic thermometers based on silica fiber-Bragg-grating technology supplied by a commercial company, as well as preliminary testing results of a silicon ring-resonator thermometer developed at the National Research Council of Canada. The main purpose of showing these two examples is to highlight some of the challenges that need to be addressed if photonic thermometers are to replace thermocouples or platinum resistance thermometers in metrology laboratories and other environments where high accuracy and stability are required, namely the influence of packaging on the sensor’s performance and the need for rigorous testing to be done in a temperature metrology lab.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".