Modeling biophysical and anthropogenic effects on soil erosion over the last 2,000 years in central Mexico
Bibliographic record
Abstract
Abstract Erosion prediction models recreate past scenarios, assess future ones, and determine the best explanatory variables of the soil erosion process. They are widely used and contribute valuable data for landscape management. This paper presents an estimation of soil erosion in the Teotihuacan Valley Basin in central Mexico, assessing its response to biophysical and anthropogenic components during 4 periods within the past 2,000 years. The valley has undergone past and recent anthropogenic erosion and, during the past 2 millennia, has experienced a marked variation in precipitation, variations in land use, soil management, and to a lesser extent, variations in soil type. With the use of the Water Erosion Prediction Project model, we estimated how the above‐mentioned parameters affect soil losses under 4 scenarios: (a) humid conditions (900 mm yr−1) during the Teotihuacan Period (1–650 CE), (b) dry conditions (370 mm yr−1) during the Aztec Period (1325–1521 CE), (c) humid conditions (900 mm yr−1) during the Aztec Period, and (d) present conditions (after 1970 CE; 560 mm yr−1). Comparison of scenarios and a principal component analysis of soil loss according biophysical components showed topography to be the most closely related parameter to soil erosion. Land use and soil type also showed a relationship with soil erosion, particularly during the Aztec Period; climate change did not appear to be the most significant factor in soil loss. Estimation of soil erosion by means of models is an inexpensive way to find answers to future challenges concerning soil erosion in a changing environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".