Bibliographic record
Abstract
Laser induced cooling of solids or optical refrigeration is an area of optical science investigating interaction of light with condensed matter. This addresses a very important practical issue: design and construction all optical solid-state cryocoolers, which are compact devices, free from mechanical vibrations, moving parts, or fluids. They are based on reliable diode pump technology and in the most part free from electromagnetic interference in the cooled area. The optical cryocooler has a broad range of applications such as in the development of biomedical sensing, magnetometers for geophysical sensors and other sensors, satellite instrumentations where compactness and the lack of vibration are key parameters. The operation of these devices is based on anti-Stokes fluorescence also known as luminescence upconversion , in which light quanta in the red tail of the absorption spectrum are absorbed in a material from a pump laser and by adding thermal energy, blue-shifted photons are spontaneously emitted. Laser cooling of solids can be realized in rare-earth doped low phonon energy glasses and crystals as well as in direct band gap semiconductors. Both of these areas are very interesting and important and are discussed in this article.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".