Detecting grave sites from surface anomalies: A longitudinal study in an Australian woodland
Bibliographic record
Abstract
Forensic investigations of single and mass graves often use surface anomalies, including changes to soil and vegetation conditions, to identify potential grave locations. Though numerous resources describe surface anomalies in grave detection, few studies formally investigate the rate at which the surface anomalies return to a natural state; hence, the period the grave is detectable to observers. Understanding these processes can provide guidance as to when ground searches will be an effective strategy for locating graves. We studied three experimental graves and control plots in woodland at the Australian Facility for Taphonomic Experimental Research (Sydney, Australia) to monitor the rate at which surface anomalies change following disturbance. After three years, vegetation cover on all grave sites and control plots had steadily increased but remained substantially less than undisturbed surroundings. Soil anomalies (depressions and cracking) were more pronounced at larger grave sites versus the smaller grave and controls, with leaf litterfall rendering smaller graves difficult to detect beyond 20 months. Similar results were observed in two concurrent burial studies, except where accelerated revegetation appeared to be influenced by mummified remains. Extreme weather events such as heatwaves and heavy rainfall may prolong the detection window for grave sites by hindering vegetation establishment. Observation of grave-indicator vegetation, which exhibited abnormally strong growth 10 months after commencement, suggests that different surface anomalies may have different detection windows. Our findings are environment-specific, but the concepts are applicable globally.
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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 | 0.001 | 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 | 0.009 |
| 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 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".