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.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".