Aerial, Surface, and Subsurface Multimodal Mapping in Coastal Peru
Bibliographic record
Abstract
ABSTRACT This article describes a series of steps to integrate multiple modes of archaeological mapping in arid and agricultural settings. We use the coastal region of Peru as a case study and share our recent field experience at Cerro San Isidro, a multicomponent hill site located in the agriculture-intensive and mid-elevation (about 500 m asl) Moro region of the Nepeña Valley. In June and July 2022, we spent eight weeks deploying a combination of drone aerial imagery, pedestrian GPS reconnaissance, and GPR survey to map the surface and subsurface features at the site and in the adjacent agricultural fields. Our efforts suggest that the ancient settlement extended over an area of at least 50 ha, well beyond the visible surface architecture. Using a multimodal approach to confirming the partial destruction of archaeological vestiges by modern agricultural encroachment is both time-effective and noninvasive. The article offers insights from our experience, including the sequence of field operations, technical troubleshooting, and the collection and integration of datasets. We discuss the methodological potential and implications of this combination of multimodal mapping and its deployment in coastal Peru, a region that, like many others in the world, is increasingly subject to rapid agricultural expansion and other anthropogenic developments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".