Bibliographic record
Abstract
Visual elements such as grids, labels, and contour lines act as reference structures that support the primary information being presented. Such structures need to be usefully visible, but not so obtrusive that they clutter the presentation. Visual designers know how to carefully manage transparency and layering in an image to balance these elements. We want the presentation of these structures in complex, dynamic, computer-generated visualizations to reflect the same subtlety and comfort of good design. Our goal is to determine the physical, perceptual, and cognitive characteristics of such structures in a way that enables automatic presentation. Our approach to this problem does not try to characterize "ideal” or "best,” but instead seeks boundary conditions that define a range of visible yet subtle legibility. All presentations that are clearly bad lie outside of this range, and can easily be avoided. In this paper, we report three experiments investigating the effects of grid color and spacing on these boundary conditions, defined by manipulating the transparency (alpha) of thin rectangular grids over scatter plots. Our results show that while there is some variation due to user preference and image properties, bounding alpha allows us to reliably predict a range of usable yet unobtrusive grids over a wide variety of conditions.
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.
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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".