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
There is great interest in improving the visual quality of projectedimagery. In particular, for image enhancement, we would assertthat text and non-text regions should be enhanced differently inseeking to maximize perceived quality, since the spatial and statis-tical characteristics of text and non-text images are quite distinct.In this paper, we present a text enhancement scheme based on anovel local dynamic range statistical thresholding. Given an inputimage, text-like regions are obtained on the basis of computing thelocal statistics of regions having a high dynamic range, allowing apixel-wise classification into text-like or background classes. Theactual enhancement is obtained via class-dependent Wiener filter-ing, with text-like regions sharpened more than the background.Experimental results on four challenging images show that the pro- posed scheme offers a better visual quality than projection with- out enhancement as well as a recent state-of-the-art enhancementmethod.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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