Asymptote: Interactive TEX-aware 3D vector graphics
Bibliographic record
Abstract
Asymptote is a powerful descriptive vector graphics language for technical drawing recently developed at the University of Alberta. It attempts to do for figures what (LA)TEX does for equations. In contrast to METAPOST, Asymptote features robust floatingpoint numerics, high-order functions, and a C++/ Java-like syntax. It uses the simplex linear programming method to resolve overall size constraints for fixed-sized and scalable objects. Asymptote understands affine transformations and uses complex multiplication to rotate vectors. Labels and equations are typeset with TEX, for professional quality and overall document consistency. The feature of Asymptote that has caused the greatest excitement in the mathematical typesetting community is the ability to generate and embed inline interactive 3D vector illustrations within PDF files, using Adobe’s highly compressed PRC format, which can describe smooth surfaces and curves without polygonal tessellation. Three-dimensional output can also be viewed directly with Asymptote’s native OpenGL-based renderer. Asymptote thus provides the scientific community with a self-contained and powerful TEX-aware facility for generating portable interactive three-dimensional vector graphics. 1
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 0.034 |
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".