The Green Deer: Chaya as a Potential Source of Protein for the Ancient Maya
Bibliographic record
Abstract
Archaeologists have used isotope analysis (δ13C, δ15N) of the collagen of human bones, as well as knowledge of available nutrients, to infer that the diet of the ancient Maya was drawn from the resources of the Maya forest landscape. The interpretations have focused on plant carbohydrates from maize and protein dominated by white-tail deer. The δ15N values of bone collagen suggest that most of the protein requirements of the Maya could have been satisfied with a mixture of wild animal flesh and wild and cultivated plants including beans. Chaya, Cnidoscolus aconitifolius, domesticated before the Spanish conquest, has a high-protein content and the potential to have been a significant contributor to the ancient Maya diet. Chaya is easily propagated, is grown in home gardens by the Maya today, and is a significant part of the local traditional diet. Chaya's stable isotopic composition of carbon (δ13C) resembles that of other terrestrial plants, but its values for nitrogen (δ15N) are significantly higher. Consumption of chaya would result in slightly higher δ15N values in humans than expected from the consumption of terrestrial animals. Thus, chaya is situated well as a component of the complex, diverse, and varied diets of ancient Mesoamericans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| 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".