Classic Maya landscape adaptation, agricultural productivity, and political dynamics in the upper Belize River Valley
Bibliographic record
Abstract
The upper Belize River Valley of west-central Belize is a complex ecotone where multiple environmental zones converge around the Mopan, Macal, and Belize Rivers. The valley's natural fecundity attracted Preclassic Maya (1200/1100 BCE–CE 300) farmers to the region, fostering population growth and the formation of several Classic (CE 300–900) polities. By the Late Classic (CE 600–900) the valley represented a dense conurbation of settlement focused around four major centers, each of these polities contained numerous intermediate elite headed districts of commoners. Evidence for political disintegration and demographic decline appeared around CE 750, coinciding with increasing drought, culminating in the complete collapse of these polities and a regional demographic crash around CE 1000. In this study, we combine environmental data and agricultural modeling to assess polity- and district-level agrarian productivity in the polities of Baking Pot, Cahal Pech, Lower Dover, and Yaxox. Our agricultural modeling indicates these polities could have generated significant agricultural surpluses under stable climatic conditions and low population density. Increasingly variable climate during the ninth to the eleventh century CE, however, impacted traditional rain fed milpa cultivation on the upland hillslopes in the south of the region, prompting out migration. In contrast, households situated on riverine alluvium appeared to thrive during this period. The case study highlights the importance of understanding environmental factors and agricultural strategies when reconstructing past political dynamics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".