Per-Pixel Lists for Single Pass A-Buffer
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
In this chapter, the authors address different techniques to build and render from an A-buffer in real time. They focus on scenes with moderate or sparse depth complexity; the techniques present will not scale well on extreme transparency scenarios. All techniques build the A-buffer in a single geometry pass: the scene geometry is rasterized once per frame. The techniques differ along two axes. The first axis is the scheduling of the sort: when do we spend time on depth-sorting the fragments associated with each pixel? The second axis is the memory allocation strategy used for incrementally building the per-pixel lists of fragments. The authors implement all techniques in OpenGL Shading Language (GLSL) fragment programs, using the extension NV_shader_buffer_store on NVIDIA hardware to access graphics processing unit memory via pointers. They discuss the sort in local memory required for Post-Lin and Post-Open, as well as how to perform early culling with Pre-Lin and Pre-Open.
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 | 0.001 |
| 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