Ground penetrating radar used to detect drowning victims under ice
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
Every year, people drown after falling through ice on rivers and lakes. In some cases, the body of the victim floats up to the underside of the ice, making detection and recovery difficult using traditional search methods with divers. A robust and contact-less sensing system is required to locate drowning victims that does not put rescue teams at risk of falling through the ice themselves. In this paper, we demonstrate the feasibility of a ground penetrating radar (GPR) for detecting deceased drowning victims that have floated up to the underside of the ice. We placed three euthanized pigs simulating drowning victims under ice ranging in thickness from 5 to 26 cm. We dragged a GPR at 500 MHz and 1 GHz across the ice to detect the simulated victims using an autocorrelation-based detection technique. Results showed that both frequencies were able to detect the rough shape of the simulated victims at ice thicknesses up to 42 cm, with the 1-GHz data showing slightly more resolution than the 500-MHz data. These results show promise and suggest future development of an autonomous drone-based GPR detection system. Key points: Floating bodies are successfully detected under both ice and snow using a commercial ground penetrating radar system with ice depths reaching up to 26 cm in a controlled environment.The differences between using radar systems operating at/around 500 MHz and 1 GHz were not pronounced from the point of view of detection.Future studies should investigate the capabilities for detecting bodies in more realistic settings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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