A reconstruction of the Indigenous maize farming niche of Utah
Bibliographic record
Abstract
• Machine learning reconstructs the Indigenous maize farming niche of Utah. • Early Utah farmers balanced elevation-driven precipitation and the growing season. • Farmers mitigate this trade-off by diverting perennial streams in drier regions. • Early Utah farmers settled in an ideal distribution model way. Maize ( Zea mays L . ) was one of the most widespread domesticated plants in the Americas before European colonization. Despite its widespread distribution, explaining how and why ancient maize farming spread into the northern reaches of the American Southwest remains a central research question in archaeology. To understand how ancient maize spread, we need a comprehensive suitability model for maize agriculture that incorporates multiple ecological variables to accurately predict where maize farming was suitable. In this paper, we construct a species distribution model for maize, using a novel machine learning approach from ecology, to produce a suitability model for Indigenous maize agriculture in Utah (ca. 3200–500 calBP). We then explore the variation in the suitability of maize farming settlements over time. Results suggest that Indigenous maize farmers selected settlement locations to minimize the distance to perennial streams and to balance the elevation-driven trade-off between growing degree days and precipitation. By comparing locations best suited for maize agriculture over time and relative to estimates of past population density, we suggest Indigenous maize farmer settlement patterns throughout the northwestern limit of ancestral maize farming follow predictions from an ideal distribution model, where increasing competition drove farmers into less suitable farming habitats. These results help elucidate the environmental and demographic patterns that influenced the spread of Indigenous maize farming.
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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 | 0.002 | 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 | 0.005 |
| 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".