Bibliographic record
Abstract
Recursion is a programming technique in which a solution can be expressed by a subroutine invoking itself either directly or indirectly. Many problems can be expressed simply using a recursive approach, however one of the drawbacks of using recursion is that it requires a stack, and often one does not know how much stack space is needed to obtain a recursive result. Stack overflow often results in spectacular failure with strange, often unrepeatable behaviour. Paraffin is a suite of generic units that can add parallelism to iterative and recursive problems. Some of the generics involve a load balancing technique described as "work-seeking". It was found that the recursive work seeking algorithm could be extended to also provide stack safety whereby the generics monitor the amount of remaining stack space and avoid stack overflow using a technique similar to load balancing. The stack safety feature also makes it attractive to consider Paraffin for use with code destined for execution on a single core. This paper describes how the recursive work-seeking algorithm was extended to provide the stack-safety feature, and then goes on to report some performance results using the generics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".