Bibliographic record
Abstract
This review covers the recent achievements in high-power rare earth (RE)-doped fiber lasers, Raman fiber lasers, and Brillouin fiber lasers. RE-doped fiber lasers have many applications such as laser cutting, laser welding, laser cleaning, and laser precision processing. They operate in several wavelength ranges including 1050–1120 nm (ytterbium-doped fiber lasers), 1530–1590 nm (erbium- and erbium–ytterbium-doped fiber lasers), and 1900–2100 nm (thulium- and holmium-doped fiber lasers). White spaces in the wavelength spectrum, where no RE-doped fiber lasers are available, can be covered by Raman lasers. The heat power generated inside the laser active medium due to the quantum defect degrades the performance of the laser causing, for example, transverse-mode instability and thermal lensing. It can even cause catastrophic fiber damage. Different approaches permitting the mitigation of the heat generation process are considered in this review. Brillouin fiber lasers, especially multiwavelength Brillouin fiber lasers, have several important applications including optical communication, microwave generation, and temperature sensing. Recent progress in Brillouin fiber lasers is considered in this review.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".