Heuristic scheduling algorithms designed based on properties of optimal algorithm for soft real-time tasks
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Bibliographic record
Abstract
A soft real-time task is one whose completion time is recommended by a specific deadline. However, should the deadline be missed, such a task is not considered to have failed; only the later it finishes, the higher the penalty that is paid. For a set of soft real-time tasks that are to be scheduled on a single machine, our objective is to minimize the total penalty paid. Since the problem is NP-hard, it is not known whether an optimal schedule can be found in polynomial time. We prove four properties of any optimal scheduling algorithm for the problem. Then, we derive a number of heuristic algorithms which hold the properties obtained herein. The heuristic algorithms differ in the way that the tasks priorities are assigned. These algorithms assign priorities by using functions of task execution times, penalty factors or deadlines. Numerical simulations are presented to compare the penalty to be paid by each algorithm.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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