A Landscape Archaeology Approach to Understanding Household Water Management Practices of the Ancient Lowland Maya
Bibliographic record
Abstract
High-resolution airborne lidar has been employed in the Maya lowlands to examine landscape modifications, detect architectural features, and expedite and expand upon traditional settlement surveys.Another potentially beneficial-and to-date underutilized-application of lidar is in the analysis of water management features such as small reservoirs and household storage tanks.The urban center of Yaxnohcah, located within the Central Karstic Uplands of the Yucatan Peninsula, provides an ideal test case for studying how the residents of this important Maya community managed their seasonally scarce water resources at the household scale.We employ an integrative approach combining lidar-based GIS analysis of 24 km 2 of the site area, ground verification, and excavation data from five small depressions to determine their function and the role they may have played in water management activities.Our research shows that some, but not all, small depressions proximate to residential structures functioned as either natural or human-made storage tanks and were likely an adaptive component of expanding Middle Preclassic to Classic period urbanization at the site.Thus, while lidar has revolutionized the identification of topographical features and hydrologic patterns in the landscape, a combination of ground verification and archaeological testing remains necessary to confirm and evaluate these features as potential water reservoirs.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".