Adaptive radar scheduling of track updates
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
The scheduling of tracking update looks for a phased array radar is considered. A method called the Two-Slope Benefit Function (TSBF) Scheduler is formulated and requires that each tracking look have a benefit function, which specifies benefit as a function of start time. This method accounts for both look priority and target dynamics in formulating a look schedule. If the radar is overloaded with tracking look requests, the TSBF Scheduler down-selects a set of looks which can be scheduled, using a method which favours higher priority looks. Looks are scheduled to maximize the total benefit, and it is shown that the resulting maximization is equivalent to a linear program which can be solved efficiently using the simplex method. This technique attempts to optimize target tracking performance while making the best use of radar resources. An example is presented which illustrates the properties of the TSBF Scheduler and compares performance to the state-of-the-art.
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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