Detecting Buried Human Bodies Using Ground-Penetrating Radar
Bibliographic record
Abstract
Being located at the dense tectonic activity area, Indonesia has to cope with the constant risk of earthquakes. High frequency of earthquakes occurrence causes the crust instability and leads into another natural disaster such as landslides. Sometimes, the landslide avalanches are covering the high populated area destroying buildings and causing victims. Unfortunately, the treatment for the affected building and landslide victims searching are still using conventional methods. The purpose of this study is to detect buried human bodies using GPR method, so it can increase the effectiveness and the efficiency of disaster victims searching under the landslide avalanche. Ground-penetrating radar (GPR) is one of the geophysical methods that can be used to study shallow subsurface of the earth. GPR has been successfully used to locate grave and forensic evidence. However, more controlled research is needed to improve the effectiveness and efficiency of disaster victim detection that buried under landslides or earthquake avalanche. A detailed GPR survey was conducted in the Cikutra graveyard, Bandung, with corpses buried one week until two months before the survey. The radar profiles from this survey showed the clear amplitude contrast anomalies, emanated from the corpses. The strongest amplitude contrasts are observed at most recent grave compared to the older grave. We obtained the amplitude contrast at around 1.2 meters depth which is consistent with the depth of the buried corpses. In addition, the results of forward modeling of homogenous subsurface and corpses in subsurface will be presented.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".