Bibliographic record
Abstract
The ISO-10110 metrics summarize in a few figures the gist of the form error of an optical surface. The values of a few ISO-10110 metrics are usually sufficient to tell if the optical elements are of adequate quality to allow the construction of an optical system with the desired performance. In the context of tolerance analysis, surface form deviations (SFD) are simulated by adding a random sum of generic surfaces on top of a nominal surface. A Matlab-based tool was created to convert the 2D continuous mathematical models of SFD into ISO-10110 metrics. The tool works directly on the raw data of the Monte-Carlo files produced by OpticsStudio during the tolerance analysis process. Not only ISO-10110 metrics are calculated by the tool, but also many mechanical metrics and other optical metrics. The entire set of metrics is calculated for all the surfaces, elements or groups and this for each of the Monte-Carlo optical configurations. Both rotationally symmetric and cylindrical surfaces can be processed by the tool. The calculation of the irregularities is done by a decomposition of the SFD functions into Zernike polynomials and bivariate Legendre polynomials respectively for rotationally symmetric and cylindrical surfaces. A bar graph is used to display all the results of a given type on a single graph. A distance correlation is implemented in the tool to help identify the worst sources of performance degradation. Therefore, the tool can also be used for the iterative tightening of the most significant tolerance operands during the entire tolerance analysis.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.033 |
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".