Photochemically Engineered Large‐Area Arsenic Sulfide Micro‐Gratings for Hybrid Diffractive–Refractive Infrared Platforms
Bibliographic record
Abstract
The cover picture referring to article number 2300241 by Myungkoo Kang, Tian Gu, and co-workers showcases photochemically induced micro-gratings for a hybrid diffractive–refractive lens. These lenses, composed of diffractive and refractive elements, aim to achieve achromatic optics with significantly reduced size, weight, and power consumption. Here, metastable As2S3 chalcogenide glasses underwent direct laser writing and subsequent selective etching to create diffractive micro-gratings. The grid on the lens’ surface represents photochemically induced micro-gratings, while the surrounding residue indicates ongoing selective etching. Incident parallel beams converge into a single focal point upon passing through the lens. (Cover illustration: courtesy of Ella Maru Studio.)
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".