Common‐ and multi‐offset ground‐penetrating radar study of a Roman villa, Tourega, Portugal
Bibliographic record
Abstract
Abstract A ground‐penetrating radar (GPR) survey was conducted at the site of the first to fourth century AD Roman villa of Tourega, near Évora, Alentejo region, Portugal. This site has been partially excavated, revealing a bathhouse complex consisting of a tank reservoir, multiple bathing rooms and a major corridor. The excavated portion of Tourega provides direct archaeological feedback as to the size, shape, depth and orientation of structures revealed by GPR surveys. A 4500 m2 area surrounding the known site was surveyed using common‐offset 500 MHz and 200 MHz pseudo‐three‐dimensional acquisition. Amplitude time‐slice analysis reveals a large number of well‐defined additional structures confirming a much broader extension of the site. For shallow buried rectilinear structural targets, the high frequency 500 MHz common‐offset data provide excellent imaging. However, limited depth penetration at 500 MHz necessitated a lower frequency of investigation; therefore 200 MHz common‐ offset and multi‐offset data were also acquired. Two 200 MHz multi‐offset GPR profiles were obtained over the large double‐walled structure identified on the time‐slices, acquired using a newly proposed multi‐offset acquisition technique – rapid multi‐offset acquisition. This acquisition technique uses the existing single‐channel GPR system, is extremely cost‐effective, and easily acquires a high fold. Rapid multi‐offset acquisition provides an average time saving up to five times that of stationary multi‐offset profiling. The 200 MHz multi‐offset profiles provide a greater depth penetration and enhanced structural detail (i.e. improved imaging of dipping structures) than comparable 200 MHz common‐offset profiles. Post‐stack, inverse‐Q filtering increases vertical resolution and interpretability of the multi‐offset sections via removal of wavelet dispersion. Although multi‐offset data are less feasible for characterizing this site in three dimensions because of the large time commitment in data collection, two multi‐offset profiles over key structures of interest improved the final site interpretation, with enhanced image quality and higher resolution than comparable common‐offset profiles. The combined results of the multi‐offset survey with a common‐offset grid should encourage a more frequent use of multi‐offset data at archaeological sites when the depth extent of the archaeological site is uncertain. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".