Understanding Ancient Maya Agricultural Terrace Systems through Lidar and Hydrological Mapping
Bibliographic record
Abstract
Abstract Lidar has been revolutionary to the understanding of ancient Maya anthropogenic landscapes. This is no more apparent than in western Belize, where the scale and resolution of these images have identified vast networks of agricultural terrace systems, revealing their true extent and density. This paper moves beyond the initial identification of terrace distribution to use lidar imagery in combination with digital elevation models (DEM) and hydrological mapping programs (Arc Hydro) to explore the drainage catchments associated with agricultural terraces at the ancient Maya site Waybil, a minor center within the Minanha polity in the North Vaca Plateau. We specifically address how the builders of these relic agricultural features worked with the natural topography to manipulate and create more effective catchments and drainage routes. Results from hydrological modeling describe how terraces created smaller drainage catchments by increasing lower levels of flow accumulation and redirecting routes laterally across the topography. Over a decade of research within this sub-region provides the necessary survey, excavations, and chronological datasets to accurately assess the efficacy of the combined methods for relic terrace drainage analysis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".