Contributions of Multi‐Method Geophysical Survey to Archaeological Research at the Battlefield of Waterloo
Bibliographic record
Abstract
ABSTRACT Archaeological prospection is continually expanding into new frontiers, examining increasingly large areas, diverse environmental contexts and varying site types. One area that has received only limited focus is historic battlefields. This paper presents results from large‐scale geophysical surveys (> 100 ha) at the Napoleonic battlefield of Waterloo (1815) in Belgium, using fluxgate magnetometry and frequency‐domain electromagnetic induction. Despite its international historical significance, professional archaeological research at the battlefield is still in its infancy. We demonstrate how important insights can be gained by using geophysical methods for identifying features and artefacts related to the battle and for developing an understanding of the various influences acting on the present landscape. The largest survey of its kind undertaken on a single battlefield site, this approach holds particular potential for battlefield archaeology, given the subtle and low‐density nature of the sought‐after targets and the extensive area of the site. Such an approach can mitigate (though not entirely resolve) challenges of resolution and scale associated with other methods of investigation. Using a representative range of examples from Waterloo, we consider successes and challenges in undertaking geophysical surveys on battlefield sites. An integrated approach that incorporates targeted sampling and other forms of ancillary data is emphasized for a more robust interpretation of noninvasive sensor data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".