Electrical resistivity tomography of simulated graves with buried human and pig remains
Bibliographic record
Abstract
Rigorous field assessment in different soil types and climates comparing simulated graves with pig remains and human remains are needed to assess the capabilities and limitations of electrical resistivity tomography (ERT) as a tool to search for unmarked graves. Our study assesses the ERT signals from graves with pig and human remains in a cold, humid continental climate with sandy soils. Two sets of three experimental graves were established: the first set consisted of two graves containing human remains and an empty grave serving as a control, while the second set consisted of two graves with pig remains and a second empty grave. ERT measurements were conducted prior to establishing the graves and were repeated 10 times over seventeen months, except for winter months when measurements were impossible. Each time we acquired eight 18 m long ERT transects using a dipole-dipole electrode array with a unit electrode spacing of 0.5 m and the transects spaced 1 m apart. The measured electrical resistivity decreased for all graves by 14-22 % for measurements conducted up to two months after burial. No further decrease was observed in the control, while resistivity in the graves with human and pig remains continued to decrease by 45-52 % up to the end of our study, seventeen months after burial. The resistivity anomaly in the pig graves shows a contrasting anomaly that is broader than that of the human remains. Our study thus validates the sensitivity of ERT to graves in cold, humid climates with sandy soil.
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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) | 0.000 | 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".