Detection of Single Burials Using Multispectral Drone Data: Three Case Studies
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".