Bibliographic record
Abstract
The scheduling of tracking and surveillance looks for multifunction radar is considered. A technique called the sequential scheduler is proposed, whereby tracking looks and high-priority surveillance looks are scheduled first, and lower-priority surveillance looks are then scheduled to occupy gaps in the radar time line. A method called the two-slope benefit function (TSBF) sub-scheduler is used and requires that each tracking look and high-priority surveillance look has 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 sub-scheduler down-selects a set of looks that can be scheduled, using a method that favours higher priority looks. Looks are scheduled to maximise the total benefit, and it is shown that the resulting maximisation is equivalent to a linear program which can be solved efficiently using the simplex method. A technique called the gap-filling sub-scheduler is used to schedule lower-priority surveillance looks. An example is presented which illustrates the properties of the sequential scheduler.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".