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
Abstract Image mosaic effects are wildly applied in print media, domestic decoration, and many image beautification applications. However, the current image mosaic methods are mostly based on fixed‐size image tiles, simple color adjustment, and irregular image segmentation, which are inaccurate and very time‐consuming. In this paper, we present a graphics processing unit‐accelerated perceptual mosaic using density tiles replacement and brightness lighting optimization, keeping original image structure details and providing more expressive visual effects. Automatic density replacement map segmentation and color‐based region tiles replacement are performed to facilitate the mosaic. Delicate brightness optimization and perceptual color correction are further applied to enhance expressive lighting effects. We also consider the salience perception of images and similarity correlation among neighboring tiles for our perceptual mosaic. The experimental results have shown the efficiency and high‐quality performance of our density‐enhanced perceptual mosaic on graphics processing unit. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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