The challenges of signal interpretation of burials in ground‐penetrating radar
Bibliographic record
Abstract
Abstract The identification of unmarked graves and burials is one of most common applications of ground‐penetrating radar (GPR) in archaeology. Despite a high frequency of use and a long history of experimentation, there appears to be considerable variability on what indicates a burial in GPR data—likely a consequence of heterogeneity in geological contexts, age and in burial practices. Although general statements about uncertainty in GPR interpretation may be acceptable in archaeological applications, the interpretative process becomes more complicated when GPR is used to locate unmarked graves in culturally, politically and legally contested locations such as at former Indian Residential Schools (IRSs) in Canada. In this paper, we review international applications of the technique and identify trends and traits between the authors' use of GPR to identify burials. By categorizing the studies based on the GPR reflection signatures identified, our review demonstrates that there is modest consensus across the 77 documents reviewed for what represents a burial. Interrogating these findings, we identify a range of potential contributors to signal heterogeneity and outline potential steps forward to a higher confidence or more statistically robust identification of unmarked graves using GPR.
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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 | 0.043 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".