Radar Task Scheduling with Gaussian Random Shifted Start Time
Bibliographic record
Abstract
A radar task scheduling algorithm, Gaussian random shifted start time (GRSST), is proposed. The algorithm shifts each task's start time by a Gaussian distribution instead of a uniform distribution within the time window which was used in the random shifted start time (RSST) algorithm. Each task's priority is used to calculate its distribution variance. The random search is not related to the priorities. A higher priority task will have a smaller variance, so that its movable range is less than that of a lower priority task. Similar to the RSST, multiple searches help to find the solution with the lowest cost. Monte Carlo simulations show that the GRSST reduces the cost significantly with much less searches, which saves a lot of computation time too. The GRSST with 50 searches provides a better solution which costs around 2 times less than the RSST with 350 searches. The GRSST's average computation time is reduced to 1ms from 7ms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".