Priority-Based Stippling and its Stylization Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents a new and efficient automatic method for structure-preserving stippling. The core idea is to concentrate on structure preservation by using a priority-based scheme that treats extremal pixels first and preferentially assigns positive error to lighter pixels and negative error to darker pixels, emphasizing contrast. The use of a nonlinear spatial function to shrink or exaggerate errors implicitly provides global adjustment of density. Personal adjustment respects contrast and hence allows people to preserve structure even with few stipples. Beyond the advantage of good structure preservation, the algorithm provides many variations to extend personal stippling to other artistic styles. In addition, it is demonstrated that variations on priority-based schemes, by a multiple-stage process, can provide flexibility to promote different kinds of interesting features. This article explores a variety of stylized effects, including heightening, scratchboard, and line drawing, all within the unifying framework of stippling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it