Aerial, Surface, and Subsurface Multimodal Mapping in Coastal Peru
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it