Compression Testing of Three Soft Lens Polymers with a Simulated Fingernail
Bibliographic record
Abstract
An analysis of the mechanical properties of finished lenses utilizing compression testing identifies factors contributing to soft lens damage. Sauflon 70, Snoflex 50, and Toyo 515 PolyHEMA lenses, all of plano power and equal thicknesses were compressed within the optical zone by loads exerted by a simulated fingernail made of guitar pick material. Six lenses of each polymer were used for each of three tests. It was found that Toyo 515 PolyHEMA had a relatively lower compressive strength than the other two non-HEMA materials; that the Sauflon 70 had the least ability to recover once compressed; and that all three polymers did not appear to recover their previous compression strength after undergoing a dehydration/rehydration cycle. In the case of the Toyo 515 lenses, this last result was confirmed statistically.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".