Modeling the 3-Micron Class Er-Doped Fluoride Fiber Laser With a Cubic Energy Transfer Rate Dependence
Bibliographic record
Abstract
We propose an energy transfer model with a cubic atomic population dependence to accurately model the behavior of various reported high-power erbium-doped fluoride fiber lasers operating near 2.8 microns. We first show that the previously introduced weakly interacting (WI) and strongly interacting (SI) models are not adequate for precisely modeling such high-power erbium-doped fluoride fiber lasers. We compare results obtained with the WI and SI models to the proposed model by simulating 4 different highly doped (7 mol.%) fiber lasers previously reported in the literature. Laser efficiencies and powers are reproduced with great accuracy. In addition, four other fiber laser systems based on erbium concentrations varying from 1–6 mol.% are also simulated with good accuracy using the proposed model with the exact same set of spectroscopic parameters, which confirms its validity for various erbium doping concentrations. Redshifting of laser wavelength is also taken into account by considering the full cross section spectra and computing signal powers over several wavelength channels.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".