Using Ground Penetrating Radar and Resistivity Methods to Locate Unmarked Graves: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The location of unmarked graves in forensic and archaeological investigations is legally and culturally important. In a forensic context, locating covert burials of missing persons can provide closure to the family, as well as facilitating the successful prosecution of the individual(s) responsible. Archaeologically, burials provide an important source of information about health, diet, physical anthropology, and culture. Despite the importance of these features, the location of unmarked graves with conventional archaeological and forensic techniques, such as excavation, is difficult and expensive. As a result, geophysical techniques have been widely applied to the location of unmarked graves as they are non-invasive, cost and time effective, and avoid the unnecessary disturbance of human remains. This article brings together the literature on ground penetrating radar (GPR), and two resistivity methods, electrical resistivity tomography (ERT) and fixed probe resistivity (FPR), on their ability to locate burials and reviews their use in forensic and archaeological investigations. This paper aims to provide law enforcement personnel, archaeologists, geophysicists, and interested academics with an overview of how these techniques work, how they have been previously applied to grave detection, and the strengths and weakness of these methods.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it