Toward Low-Loss Mid-Infrared Ga<sub>2</sub>O<sub>3</sub>-BaO-GeO<sub>2</sub> Optical Fibers: Solving 30 Years of High Losses
Bibliographic record
Abstract
Following the exceptional development of low-loss silica fibers in the 1970s, the emergence of high-speed long-distance telecommunication systems and high-power fiber lasers have revolutionized our daily lives [1]. However, silica fibers do not transmit light beyond$2.5\ \mu \mathrm{m}$and therefore cannot be employed for applications in the so-called mid-infrared (MIR) range. As a result, most MIR glass-based devices use fluoride or chalcogenide glasses (FCGs). However, the development of FCGs-containing devices is laborious due to either bad crystallization and hygroscopicity resilience or poor mechanical properties [2]. Moreover, even though their low thermal property is an important advantage for their fabrication, it becomes a disadvantage for the development of stable and powerful fiber lasers for instance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".