Detection of Single Burials Using Multispectral Drone Data: Three Case Studies
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
Natural burials are interments where a body is buried without embalming fluids or coffins. These burial grounds are ideal locations for retrospective multispectral analysis of non-conventional single burials as the age and location of each grave is documented. The detection of disturbed soil under the influence of human decomposition has been well-studied, but lacks the temporal component needed for characterising simulated clandestine burials. A critical gap in the literature is how these burials re-vegetate and to what extent soil profiles re-establish over years or decades. Multispectral drone data from three natural burial sites in southern U.K. are documented here, with trends in re-vegetation from bare soil to full recovery in graves as old as 2005. As with many burial detection techniques, environmental influence is a limiting variable to universal use of this method. However, we suggest a timeline over which single burial sites in this location reach detection limits and possible reasons for variations in these limits.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it