Detection of an experimental mass grave over time and at different spatial scales in a temperate environment
Bibliographic record
Abstract
In the past decades, the detection of clandestine mass graves has become a topic of high interest for the international forensic community. Hyperspectral remote sensing may provide complementary and novel techniques to detect mass graves in regions with human conflict by detecting changes in site surface reflectance, which can potentially be different from a non-grave area. In this research study, I assessed differences in spectral reflectance between an experimental mass grave and a non-grave in a temperate environment at three different spatial scales: leaf level and plot level using field spectroscopy and airborne hyperspectral imagery. To test the application of hyperspectral remote sensing as a tool in the detection of mass graves, three experimental study sites were established in Ottawa, Ontario, Canada: an experimental mass grave containing pig carcasses (Sus Scrofa domesticus) at one meter depth, a reference site containing only disturbed soil, and an undisturbed control site. Soil and vegetation samples and spectral data using field spectrometry and airborne hyperspectral imagery were collected in the first 15 months post-disturbance. The main findings of this research show that differences in spectral reflectance depend on spatial scale, disturbance stage and time in the growing season. Overall differences were found between the grave and control in soil chemistry, vegetation pigmentation and spectral reflectance throughout the study period. In the first 13 months post-disturbance, differences in soil chemistry (e.g. calcium and manganese), vegetation pigmentation (i.e. chlorophyll and carotenoids), and spectral reflectance between the mass grave and reference can be attributed to the overall site disturbance and not as a result of the decomposition process. In contrast, 13 months after burial there are differences in soil chemistry (i.e. ammonium, nitrate, and available phosphorus) and vegetation pigmentation between the mass grave and reference. In terms of spectral reflectance, differences were found along the 400 – 700 nm wavelength range between mass grave and reference during this period. It was also found that the combination of different vegetation indices on airborne imagery increases the spectral separation between mass grave, reference and control depending on time since disturbance. Given that spectral differences emerge towards the end of the data collection, detectable differences between the mass grave and the reference may be delayed due to (1) a slow cadaver decomposition rate and/or (2) the depth of burial that provides a greater barrier to nutrient uptake in surficial plants as previously shown in others studies for deep and shallow graves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".