Bibliographic record
Abstract
The authors investigate four different concepts which are candidates to resolve the hidden surface removal problem without the use of priorities. All four are based on determining a linear depth order consistent with the partial order of primitives in a span. A span is a small subimage of the image being generated. The first approach examines parallel sorting of all the depth values of all primitives in a span. The second approach, for each pixel in a span, builds a list of all the primitives in that span, totally ordered by depth. These lists represent the partial order of the primitives, and are later enumerated to give a total order. The third approach performs up to k pairwise comparisons in parallel between primitives to determine if a depth relationship exists. If one exists, the pair is stored in a partial order graph which is later enumerated to create a total order. The fourth approach involves the use of a pipeline of length k, which, at any given time, contains up to k primitives which are closest. If there are more than k primitives in a span, more than one iteration is needed. The fourth approach appears to be the most promising, even though there are situations in which it generates an incorrect ordering.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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.
How this classification was reachedexpand
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) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".