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
Environment matting is a technique to extract the environment matte which is used to describe how an object reflects and refracts the environment light. In this paper, we propose a novel environment matting method to obtain the environment matte of a real scene. Previous methods use different backdrops as the calibration patterns and search for the environment matte in the spatial domain. In our method, however, a series of background images displayed on a screen sequentially in time are interpreted as signals. The frequency similarity of these signals is used as the searching criterion. The frequencies of these signals are not changed when they interact with the foreground objects and thus can be used to extract the environment matte. While using correspondence in the spatial domain in existing approaches is prone to error, using frequency correspondence is not. Thus, our approach is robust to noise and can easily deal with some of the complex light transport phenomena which cannot be easily handled using current methods. The experimental results are very encouraging.
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