Evaluating subsurface damage in optical glasses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hard brittle materials (e.g. glasses and ceramics) increasingly appeal to general interests because of their excellent physical, mechanical and chemical properties such as super hardness and strength at extreme temperature and chemical stability. The precision manufacturing of these materials is primarily achieved by grinding and polishing, which generally employs abrasives to wear the materials. With this manufacturing technology, the materials are removed due principally to the fracture of brittle materials, which will leave a cracked layer on the surface of manufactured components, namely subsurface damage (SSD). The subsurface damage affects the strength, performance and lifetime of components. As a result, investigation into the subsurface damage is needed. A host of characterizing techniques have been developed during the past several decades. These techniques based on different mechanisms provide researchers with invaluable information on the subsurface damage in various materials. In this article the typical SSD evaluation techniques are reviewed, which are regularly used in optical workshops or laboratories.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it