Sondondo's ancient agriculture as reflected in microfossils from terraced soils. Procedures to understand socio-economical changes in Peru
Bibliographic record
Abstract
In Peru, archaeological studies of Andean agriculture have generally focused on archaeobotanical remains, agrarian technologies, or the social and economic dimensions of agricultural practices. Most of these studies have relied on typological or structural approaches, leading to interpretations grounded in relative chronologies and broad generalizations, especially for periods of complex social development, such as those associated with the Huari and Inca Empires. However, new interdisciplinary methodologies in agroarchaeology offer new opportunities to investigate the cultivation sites directly, moving beyond an exclusive reliance on ethnographic or ethnohistorical analogies. This paper presents the first multiple microfossil analysis from soils in agricultural terraces from the Sondondo Valley, Department of Ayacucho, Peru. We detail the application of this innovative methodological approach, which allowed us to reconstruct the chronology of terrace construction and use, and provided insight into the social implications of these agricultural practices. Furthermore, we challenge prevailing assumptions about the standardization of terraces and the demand for maize cultivation during the expansion of the Inca Empire in the Southern Andes. Our study effectively demonstrates how sociopolitical transformations can be assessed by studying past agricultural landscapes.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".