The Application of Remote Sensing for Detecting Mass Graves: An Experimental Animal Case Study from Costa Rica*
Bibliographic record
Abstract
Detection of mass graves utilizing the hyperspectral information in airborne or satellite imagery is an untested application of remote sensing technology. We examined the in situ spectral reflectance of an experimental animal mass grave in a tropical moist forest environment and compared it to an identically constructed false grave which was refilled with soil, but contained no cattle carcasses over the course of a 16-month period. The separability of the in situ reflectance spectra was examined with a combination of feature selection and five different nonparametric pattern classifiers. We also scaled up the analysis to examine the spectral signature of the same experimental mass grave from an air-borne hyperspectral image collected 1 month following burial. Our results indicate that at both scales (in situ and airborne), the experimental grave had a spectral signature that was distinct and therefore detectable from the false grave. In addition, we observed that vegetation regeneration was severely inhibited over the mass grave containing cattle carcasses for up to a period of 16 months. This experimental study has demonstrated the real utility of airborne hyperspectral imagery for the detection of a relatively small mass grave (5 m(2)) within a specific climatic zone. Other climatic zones will require similar actualistic modeling studies, but it is clear that the applications of this technology provide the international community with both an early detection tool and a tool for ongoing monitoring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".