Generalised formulations for minimum distance trajectory in patrolling problems
Why this work is in the frame
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Bibliographic record
Abstract
In this study, three general formulations are presented for trajectory optimisation in patrolling problems. In the traditional patrolling problem, some basic assumptions are made (often implicitly). For example, it is known how many robots and how many starting depots exist. Furthermore, the starting depots are assumed to be pre‐specified. Each of the three formulations provided here relaxes some (or all) of these assumptions, hence generalising the patrolling problem. A group of robots are supposed to travel through a number of nodes (viewpoints) in such an order so that the total travel distance is minimised. This problem is, in fact, a variant of the Travelling Salesman Problem and is called Multidepot multiple Travelling Salesman Problem. The effectiveness of the approach is demonstrated by comparing the results with those in the literature.
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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 | 0.001 | 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