Integrating remote datasets to identify precontact architecture and settlement patterns along the Wampu River system, Eastern Honduras
Bibliographic record
Abstract
In our era of remote sensing archaeology and legacy datasets, multiple lines of evidence should be integrated to establish a large-scale view of threatened Indigenous landscapes. This study brings together several datasets derived from airborne lidar, satellite imagery, and pedestrian survey to present the first large-scale (>650 km2) synthesis of the settlement archaeology of the Wampu River system, a part of eastern Honduras with limited archaeological work. The purpose of this study is to: 1) connect pedestrian and airborne lidar surveys within the Wampu River system; 2) identify patterns in archaeological sites and architecture; 3) characterize the anthropogenic landscape of the region, and to suggest areas for future work in this understudied part of the Americas. Results include the identification of 72 archaeological sites, 55 of which were previously unreported, and the creation of a basic classificatory scheme for site organization. Other findings include the preliminary observations that water, topography, and seasonality likely structured settlement patterns, and that there are emerging patterns in site orientation. We also consider how eastern Honduras could contribute to broader discussions about tropical settlement patterns. Overall, this study demonstrates that eastern Honduras has the potential for future research identifying extensive settlements in the centuries before European arrival, thus contributing to a more complex understanding of the extent and diversity of Indigenous populations in the Americas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".