Soil development and ancient Maya land use in the tropical karst landscape: Case of Busiljá, Chiapas, México
Bibliographic record
Abstract
Abstract The soil mantle of the tropical karstic landscapes of Southern Mexico was shaped by specific processes of pedogenesis and long‐term human impacts of ancient Maya agriculture. To understand the interaction between natural and human‐induced soil‐forming processes in the calcareous mountains of Chiapas state, we studied soil toposequences around the Classic Maya site of Budsilhá and related them to the archaeological evidence of settlement and land‐use distribution. Soil chemical analysis, micromorphological observations, and clay mineral identification were carried out in key soil profiles at the main geoforms. Limestone hills are occupied by shallow Rendolls which are usually perceived as incipient soils. However, high content of silicate clay composed of kaolinite and vermiculite and ferruginous clayey soil material observed at macro‐ and microscale backed the hypothesis that these soils were formed from the residues of thick Terra Rossa after their erosion. Swampy lowlands are occupied by thick clayey gleyic soils with clay mineral assemblages similar to those in the upland Rendolls. We suppose that the mineral matrix of the lowland soils is largely derived from the pedosediments of eroded upland Terra Rossa, which lost original ferruginous pigmentation and aggregation due to redoximorphic processes. Some wetland soils contain neoformed gypsum that is atypical for humid tropics; sulfide‐sulfate transformation under fluctuating redox conditions could promote gypsum synthesis. Ancient Maya land use was closely related to soil‐geomorphic conditions: settlements with homegardens occupied calcareous hills, whereas the primary agricultural domain was developed on lowland soils after their drainage by artificial canals.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".