Top-down and bottom-up water management: A diachronic model of changing water management strategies at Angkor, Cambodia
Bibliographic record
Abstract
The Greater Angkor region, in northwestern Cambodia, was home to several successive capitals of the Khmer Empire (9th to 15th centuries CE). During this time, the Khmer developed an extensive agricultural and water management system characterized by top-down state-sponsored hydraulic infrastructure. Archaeological evidence now shows that the well-documented state temples and water management features formed the core of an extended settlement complex consisting of many thousands of ponds, habitation mounds, and community temples. These community temples are difficult to date, and so far, the lack of chronological resolution in surface archaeological data has been the most significant challenge to understanding the trajectory of Angkor’s growth and decline. In this paper, we combine heterogeneous archaeological datasets and create diachronic models of the landscape as it was developed for agricultural production. We trace the foundation of new temple communities as they emerge on the landscape in relation to the construction of extensive state-sponsored hydraulic infrastructure. Together, these two forms of water management transformed over 1000 km2 of the Greater Angkor Region into an elaborate engineered landscape. Our results indicate that, over time, autonomous temple communities are replaced by large, state-sponsored agricultural units in an attempt by the state to centralize production.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".