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
We present an algorithm for creating digital micrography images, or micrograms , a special type of calligrams created from minuscule text. These attractive text-art works successfully combine beautiful images with readable meaningful text. Traditional micrograms are created by highly skilled artists and involve a huge amount of tedious manual work. We aim to simplify this process by providing a computerized digital micrography design tool. The main challenge in creating digital micrograms is designing textual layouts that simultaneously convey the input image, are readable and appealing. To generate such layout we use the streamlines of singularity free, low curvature, smooth vector fields, especially designed for our needs. The vector fields are computed using a new approach which controls field properties via a priori boundary condition design that balances the different requirements we aim to satisfy. The optimal boundary conditions are computed using a graph-cut approach balancing local and global design considerations. The generated layouts are further processed to obtain the final micrograms. Our method automatically generates engaging, readable micrograms starting from a vector image and an input text while providing a variety of optional high-level controls to the user.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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