Emerging technologies in the field of thermometry
Bibliographic record
Abstract
Abstract The past decade saw the emergence of new temperature sensors that have the potential to disrupt a century-old measurement infrastructure based on resistance thermometry. In this review we present an overview of emerging technologies that are either in the earliest stages of metrological assessment or in the earliest stages of commercial development and thus merit further consideration by the measurement community. The following emerging technologies are reviewed: Johnson noise thermometry, optical refractive-index gas thermometry, Doppler line broadening thermometry, optomechanical thermometry, fiber-coupled phosphor thermometry, fiber-optic thermometry based on Rayleigh, Brillouin and Raman scattering, fiber-Bragg-grating thermometry, Bragg-waveguide-grating thermometry, ring-resonator thermometry, and photonic-crystal-cavity thermometry. For each emerging technology, we explain the working principle, highlight the best known performance, list advantages and drawbacks of the new temperature sensor and present possibilities for future developments.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".