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 can visually recognize a variety of surface state. Just a quick look is sufficient to find the bath floor is dry, the road in front is slippery, the window glass is frosty, or the ornament is dusty. If a given surface state perception relies on the analysis of diagnostic image features, an effective strategy to reveal those features and the associated visual processing is to find a stimulus transformation that alters the apparent surface state. Here we report an image transformation that makes dry objects look wet. This wet filter consists of two operations: (1) Tone-remapping with an accelerating nonlinear function that renders the intensity histogram positively skewed: (2) color saturation enhancement. In an experimental test, we applied the wet filter to a variety of natural textures of the McGill Calibrated Colour Image Database. The results of a wetness rating experiment showed that the wet-filtered images were perceived as wetter than the original images. In addition, the perceived wetness depended on the variance of hue. The wet-filter was less effective for images with a small variance of hue. Optically, wetting a surface tends to increases the specular reflection. In addition, as the incoming light scatters repeatedly within the surface liquid layer, the light going out from the surface tends to be darker and more saturated. The effects of these optical changes can be simulated by the two wet-filter operations. However, positively skewed luminance histogram and high chromatic saturation may be caused by other factors — for instance, the visual scene may happen to include highly saturated glossy objects. This is presumably why hue variation matters. If the same image transformation simultaneously occurs in many different objects, the brain infers that the change likely has the same cause, such as water shower in the present case. Meeting abstract presented at VSS 2015
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) | 0.000 | 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