Suitability Models of Ancient Maya Agriculture in the Upper Usumacinta River Basin of Mexico and Guatemala
Bibliographic record
Abstract
Abstract Recent archaeological and remote sensing research in the Maya Lowlands has demonstrated evidence for extensive modification of the landscape in the forms of channeled fields and upland terraces. Scholars often assume these measures were taken primarily to intensify maize production; however, paleoethnobotany highlights a greater diversity of crops grown by the precolonial Maya. This study combines the growth requirements of 18 crops cultivated by ancient Maya farmers with lidar and other geospatial data in a suitability model that maps optimal areas for growth. These 18 crops cluster into five groups of crops with similar growth requirements. Across the study region, different groupings of crops had different suitability in and around different ancient Maya centers and agricultural features. This spatial variation in suitability reflects the heterogeneity of land resources and adaptations and contributes to existing conversations about economic and settlement organization in the study area. The results of this study serve as a foundation for future field studies and more complex spatial models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".