Relative usefulness of the Bayer ratio as an indicator of the hardness of different coatings
Bibliographic record
Abstract
Purpose: To show that with better performance of scratch-resistant coatings on plastic lenses, the use of the Bayer ratio does not appropriately discriminate between recently introduced products. Methods: Nine groups of 5 to 10 CR-39 lenses were ordered with various scratch-resistant treatments. All the lenses underwent the Bayer test on the same apparatus. Haze was measured using a Cary5000 spectrophotometer equipped with an integrating sphere. Results: The lens groups with the latest generation of anti-scratch treatments show a significant decrease in diffusion. The variability of the Bayer ratio increases depending on whether the quotient is obtained by calculating the ratio using the minimal haze values or the maximum haze values. When the Bayer ratio is greater than 10, it no longer discriminates between the products satisfactorily. Conclusions: This test, or others, will inevitably have to be refined to increase precision and give a better perspective of the quality of the next generation of scratch-resistant coatings.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".