Bibliographic record
Abstract
For queueing systems, leveraging knowledge of job sizes to perform size-based scheduling leads to policies with attractive performance characteristics. Although there is a body of literature in this area, in the interest of space, we highlight one classical result: for a single-server system, the shortest remaining processing time (SRPT) policy (priority is given to the job closest to completion) is known to minimize the mean response time ( Schrage and Miller 1966 ). Although this and related performance results have been known for some time, such size-based scheduling policies have not been deployed to any great extent in practice. One objection to their deployment is that the assumption that one knows job sizes exactly is problematic; the typical scenario would be that estimates of job sizes are available to make scheduling decisions. There is not a large literature on the study of queueing systems in which there are estimation errors for job sizes. In the next paragraph, we discuss some typical approaches and then conclude the section with two open problems of interest in this area.
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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 | 0.016 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".