INFORMATION SURFING FOR RADIATION MAP BUILDING
Bibliographic record
Abstract
We develop a control scheme for a group of mobile sensors to map radiation over a given planar polygonal region. The advantage of this methodology is that it provides quick situational awareness regarding radiation levels, which is being updated and refined in real-time as more measurements become available. The control algorithm is based on the concept of information surfing, where navigation is done by following information gradients, taking into account sensing performance and the dynamics of the observed process. It is tailored to cases of weak radioactivity, where source signals may be buried in background. We steer mobile sensors to locations which are critical points of a function that quantifies the information content of the measured signal, while * Corresponding author the time-asymptotic properties of the selected information metric facilitate the stability of the group motion. Information surfing allows for reactive mobile sensor network behavior and adaptation to environmental changes, as well as human retasking. Computer simulations and experiments are conducted to verify the asymptotic behavior of the robot group, and its distributed sensing and mapping capabilities.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".