Bibliographic record
Abstract
We present, in this paper, a hybrid algorithm which makes use of Time Warp between clusters of LPs and a sequential algorithm within the cluster. Time Warp is, of course, traditionally implemented between individual LPs. The algorithm was implemented in a digital logic simulator, and its performance compared to that of Time Warp. Resting upon this platform we develop a family of three checkpointing algorithms, each of which occupies a different point in the spectrum of possible trade-offs between memory usage and execution time. The algorithms were implemented on several digital logic circuits and their speed, number of states saved and maximal memory consumption were compared to those of Time Warp. One of the algorithms saved between 35 and 50% of the maximal memory consumed by Time Warp (depending upon the number of processors used), while the other two decreased the maximal usage up to 30%. The latter two algorithms exhibited a speed comparable to Time Warp, while the first algorithm was 30-60% slower. These algorithms are also simpler to implement than optimal checkpointing algorithms.>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".