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
Shadow maps are commonly used in real-time rendering, but they cannot be filtered linearly like standard color, resulting in severe aliasing. Variance shadow maps resolve this problem by representing the depth distribution using moments, which can be linearly filtered. However, variance shadow maps suffer from artifacts and require high-precision texture filtering hardware. We introduce layered variance shadow maps, which provide simultaneous solutions to both of these limitations. By partitioning the shadow map depth range into multiple layers, we eliminate all light bleeding between different layers. Using more layers increases the quality of the shadows at the expense of additional storage. Because each of these layers covers a reduced depth range, they can be stored in lower precision than would be required with typical variance shadow maps, enabling their use on a much wider range of graphics hardware. We also describe an iterative optimization algorithm to automatically position layers so as to maximize the utility of each. Our algorithm is easy to implement on current graphics hardware and provides an efficient, scalable solution to the problem of shadow map filtering.
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 | 0.001 |
| 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