M-TRACE: a MATLAB toolkit for animated raytracing based on scalable vector graphics illustration
Bibliographic record
Abstract
Modern optical design and simulation software, while both powerful and feature-rich, requires significant training investment for effective use, even for simple optical design tasks. By contrast, software for two-dimensional (2D) non-sequential raytracing, while sufficient for many optical design tasks and popular for educational purposes, has remained without the benefit of mature computer-aided design (CAD) technologies that are now a key feature of software dedicated to three-dimensional (3D) optical design. To overcome this limitation, we introduce M-TRACE—an open source toolkit for non-sequential 2D raytracing with scripting-based customization and animation capabilities implemented in MATLAB. This toolkit allows users to specify optical designs using tools compatible with free-form 2D illustration via scalable vector graphics (SVG), analogous to the integration of 3D CAD technologies with enterprise optical design software. The intuitive and precise drawing features of SVG illustration software (e.g., Adobe Illustrator, Inkscape) allow for a straightforward means for the creation of system designs. We intend M-TRACE to complement the spectrum of available optical design software for research, development, and education, specifically targeting design cases requiring rich visualization and/or rapid exploration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.019 |
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".