Comparison of Time-Lapse Ground-Penetrating Radar and Electrical Resistivity Tomography Surveys for Detecting Pig (Sus spp.) Cadaver Graves in an Australian Environment
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
Locating clandestine graves presents significant challenges to law enforcement agencies, necessitating the testing of grave detection techniques. This experimental study, conducted under Australian field conditions, assesses the effectiveness of time-lapse ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) in detecting pig burials as simulated forensic cases. The research addresses two key questions: (1) observability of graves using GPR and ERT, and (2) changes in geophysical responses with reference to changing climatic conditions. The principal novelty of this research is its Australian focus—this is the first time-lapse GPR and ERT study used to locate clandestine graves in Australia. The results reveal that both GPR and ERT can detect graves; however, ERT demonstrates greater suitability in homogeneous soil and anomalously wet climate conditions, with the detectability affected by grave depth. This project also found that resistivity values are likely influenced by soil moisture and decomposition fluids; however, these parameters were not directly measured in this study. Contrastingly, although GPR successfully achieved 2 m penetration in each survey, the site’s undeveloped soil likely resulted in inconsistent detectability. The findings underscore the significance of site-specific factors when employing GPR and/or ERT for grave detection, including soil homogeneity, climate conditions, water percolation, and body decomposition state. These findings offer practical insights into each technique’s utility as a search tool for missing persons, aiding law enforcement agencies with homicide cases involving covert 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.
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) | 0.000 | 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 it