Optimizing the location of sensors subject to health degradation
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
One of the main hypotheses supporting the development of cooperative unmanned systems is that the deployment of mobile assets (sensors, weapons) in groups is expected to result in a more effective mission than if conducted with a single asset. Few researches have tackled the design of autonomous decision making for teaming UxVs (unmanned air and ground vehicles) operating under degraded conditions, even though it is common knowledge that real operations are more often than not conducted in less-than-ideal conditions. We consider a team of UxVs that have for mission to persistently monitor an area. We want to ensure they perform as best as possible assuming they are subject to a limited set of degraded conditions. We propose a model to account for variable sensor effectiveness as well as a method to optimize their placement based on a cost balancing heuristic. Numerical simulation suggests accounting for sensor effectiveness improves their placement.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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