Time-Lapse Electrical Resistivity Tomography Imaging of Buried Human Remains in Simulated Mass and Individual Graves
Bibliographic record
Abstract
Electrical resistivity has been used as a noninvasive geophysical technique for locating clandestine graves and monitoring human decay within the subsurface. Detailed studies assessing resistivity anomalies due to soil disturbances and decay products associated with graves have relied on the use of proxies, such as pigs, with limited studies using real human cadavers and simulating a mass grave setting and none assessing the anomalies in 3D. In this study, we used time-lapse 2D and quasi-3D electrical resistivity distribution measured over an experimental mass grave and individual graves containing human cadavers to assess resistivity anomalies resulting from graves and the presence of decaying human remains in them. This study is part of a novel multidisciplinary mass grave experimental study with six graves consisting of a mass grave with six human cadavers, a controlled mass grave with none, three individual graves with one human cadaver each, and a control individual grave with none. Nine parallel resistivity transects which allow us to image these graves in their 3D context were acquired prior to excavation and 2 days, 1, 2, and 6 months after burial using a dipole–dipole electrode array, a unit electrode spacing of 0.5 m, and an interprofile spacing of 1m. The value of different electrode arrays and spacings in identifying the contrast between the graves was also assessed using forward models and field data. Soil sensors were installed at different locations in the graves to monitor soil electrical conductivity, moisture content, and temperature. The results of this study show an increase in electrical resistivity 2 days after burial in all graves with human remains and the control graves, which we attribute to increased soil aeration where disturbed pores are filled with air. The resistivity decreases thereafter in graves with human remains which we attribute to the formation of conductive leachates. This study validates the potential of electrical resistivity as a forensic search tool for locating both clandestine mass and individual graves and as a noninvasive monitoring technique to support human decomposition research.
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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 | 0.000 |
| 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 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".