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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.5\ \mu \mathrm{m}$</tex> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".