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 a new approach to synthetic (computer-aided) drawing with patches of strokes. Grouped strokes convey the local intensity level that is desired in drawing. The key point of our approach is learning by example: the system does not know a priori the distribution of the strokes. Instead, by analyzing a sample (training) patch of strokes, our system is able to synthesize freely an arbitrary sequence of strokes that "looks like" the given sample. Strokes are considered as parametrical curves represented by a vector of random variables following a Markovian distribution. Our method is based on Shannon's N-gram approach and is a direct extension of Efros's texture synthesis models [EL99; EF01]. Nevertheless, one major difference between our method and traditional texture synthesis is the use of such curves as a basic element instead of pixels. We define a statistical metric for comparison between different patches containing various layouts of strokes. We hope that our method performs a first step towards capturing a very difficult notion of style in drawing --- hatching style in our case. We illustrate our method by varied examples, ranging from typical hatching in traditional drawing to highly heterogeneous sets of strokes.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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