The Use of LiDAR in Understanding the Ancient Maya Landscape
Bibliographic record
Abstract
Abstract The use of airborne LiDAR (Light Detection and Ranging) in western Belize, Central America, has revolutionized our understanding of the spatial dynamics of the ancient Maya. This technology has enabled researchers to successfully demonstrate the large-scale human modifications made to the ancient tropical landscape, providing insight on broader regional settlement. Before the advent of this laser-based technology, heavily forested cover prevented full coverage and documentation of Maya sites. Mayanists could not fully recover or document the extent of ancient occupation and could never be sure how representative their mapped and excavated samples were relative to ancient settlement. Employing LiDAR in tropical and subtropical environments, like that of the Maya, effectively provides ground, as well as forest cover information, leading to a much fuller documentation of the complexities involved in the ancient human-nature interface. Airborne LiDAR was first flown over a 200 km2area of the archaeological site of Caracol, Belize, in April 2009. In April and May 2013 an additional 1,057 km2were flown with LiDAR, permitting the contextualization of the city of Caracol within its broader region and polity. The use of this technology has transformed our understanding of regional archaeology in the Maya area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".