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
A painting needs illumination to be visible. If the illumination is provided by an LCD data projector, different regions of the painting can be illuminated separately. Modern projectors have large color gamuts and can provide a wide range of illumination effects. One possible effect is to project a captured digital image of the painting onto the painting; the resulting superposition of like colors intensifies the contrast and saturation of the image. The opposite effect is to project the complement of the image onto the painting to "neutralize" it. When carefully done, with correct registration, the painting fades into a nearly uniform gray. Although a simple idea, in practice it is not trivial to accurately find the complementary color for each part of the painting, even when it is captured by a calibrated digital camera. This research examines the problems of accurately capturing the image, combining the projector gamut with typical paint reflectances, and determining the available range of complementary projector colors and the final lightness of the neutral image. The work was initially inspired by a student's fine art project, wherein computer animation was superimposed on a painting, bringing it to life.
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 | 0.001 | 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