Soil toposequences, soil erosion, and ancient Maya land use adaptations to pedodiversity in the tropical karstic landscapes of southern Mexico
Bibliographic record
Abstract
The soil mantle of the tropical karst landscapes of southern Mexico was a key resource for ancient Maya agriculture and experienced deep transformation due to long-term human impacts under changing environmental conditions. We conducted a comparative analysis of three compound soil toposequences in mountainous (Sierra de Chiapas/Middle Usumacinta Valley, Busiljá, and Chinikihá archaeological sites) and platform (NE Yucatán Peninsula, Yalahau region) karst landscapes to reconstruct general tendencies and regional variations in pedodiversity development and soil–human interactions since the Early Preclassic Period. Toposequence characterization is based on macro- and micromorphological observations, accompanied by a suite of laboratory data. Calcareous upland geoforms of all toposequences have similar soil combinations consisting of shallow Rendzina and deep red clayey Terra Rossa types of profiles. We argue that Rendzinas, now dominant in the upland soil cover, in most cases, are not a product of incipient pedogenesis on limestone; they have developed from the residues of Terra Rossa soils after their advanced erosion. Pedosediments generated by ancient soil erosion have been found in the piedmont and depression positions in the mountainous landscapes of Chiapas, as a result of lateral downslope soil removal, and in the subsurface karstic cavities in the platform of NE Yucatán, indicating vertical “soil piping.” The soils of the lowland domains show contrasting differences between the toposequences: gleyic clay–rich soils and humic alluvial soils prevail in Chinikihá and Busiljá, whereas hydromorphic carbonate soils have formed in Yalahau karstic depressions. These differences in the lowland soil properties led to divergent ancient Maya land use strategies; in Chinikihá and Busiljá, the major agricultural domain was developed in the lowlands, implying largescale artificial drainage. On the contrary, in Yalahau, mostly upland Rendzinas were cultivated, implying “precision agriculture” and “container gardening.”
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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".