Modelling Resilience: Zooarchaeological Insights into Subsistence Diversity and Land Use Practices of the Ancient Maya in the Upper Belize River Valley
Bibliographic record
Abstract
Many models have been proposed to explain the disintegration of Classic Maya polities including those based on climate change, inter-site competition, warfare, and environmental degradation. It is now clear, however, that multiple simultaneous factors were involved, and the combination of factors varied from one region of the Maya Lowlands to another during the Late to Terminal Classic periods (AD 750-900/1000). As such, each region must be examined individually to understand the processes that contributed to depopulation to explain why some regions were more resilient than others. In the upper Belize River Valley in the eastern Maya Lowlands, a series of droughts during the Terminal Classic has been attributed as one factor underlying the abandonment of centers. Using Hill diversity metrics (richness, Shannon diversity, and Simpson diversity) as measurements of diet diversity, our study analyzes Preclassic (1100/1000 BC-AD 300) and Late/Terminal Classic fauna from the sites of Baking Pot, Cahal Pech, Lower Dover, and Xunantunich to test models of environmental degradation and the adaptive cycle. The results show that the ancient Maya of the region responded to climate stresses through environmental resource management. Any rigidity in dietary preferences was mitigated by exploiting a broad spectrum of animal and plant resources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".