Approximation of Scheduling with Calibrations on Multiple Machines (Brief Announcement)
Why this work is in the frame
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Bibliographic record
Abstract
We study the scheduling problem with calibrations. In 2013, Bender et al. (SPAA '13) proposed a theoretical framework for the problem. Jobs of unit processing time with release times and deadlines are to be scheduled on parallel identical machines. The machines need to be calibrated to run jobs while a single calibration remains valid on a machine only for a time period of length T. The objective is to find a schedule that completes all jobs within their timing constraints and minimizes the total number of calibrations. In this paper, we aim to design an approximation algorithm to solve the problem. We propose a dynamic programming algorithm with polynomial running time when the number of machines is constant. In addition, we give a PTAS when the number of machines is input.
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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) | 0.000 | 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 it