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
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.< <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.
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) | 0.009 | 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 it