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
Continuous tone images must be halftoned to be displayed on binary output devices such as printers. Halftoning algorithms at low resolutions of the output hardware introduce textures into the resulting display. In this work we control halftoning texture by generating a threshold matrix from an image‐based texture. We demonstrate that processing textures by the adaptive histogram equalization algorithm approximates pixel distribution properties of traditional dither screens. Ordered dithering with the resulting threshold matrix enables us to define texture in the halftoned image. We control the appearance of this texture by a combination of the ordered dither algorithm with an error diffusion process. We present applications of texture‐based dither screens to both photorealistic and artistic rendering. In the case of photorealistic tone reproduction our technique preserves textures and edges of the original image. The ability to define an arbitrary texture enables us to introduce a variety of artistic effects, including embossing of images with textures and text, and approximation of the appearance of of conventional illustration media. We evaluate the resulting halftoning using multi‐scale edge distortion measures. Our quantitative evaluation closely corresponds to the visual observations.
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) | 0.001 | 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