Pollen indicators of slash-and-burn agriculture in forest soils and peatlands: a case study of Zvenigorod biological station (Moscow region, Russia)
Bibliographic record
Abstract
Abstract This study aimed to analyze pollen indicators of slash-and-burn agriculture in soils and aquatic deposits of the southern forest zone of Eastern Europe. Pollen spectra from buried swidden soils (500 - 1800 cal BP) were compared with coeval peat layers and modern surface soils. Swidden horizons exhibited highly distinctive pollen spectra, characterized by Betula dominance, low conifer pollen, and the presence of cultivation indicators (Cerealia) and post-fire succession taxa ( Chamaenerion, Pteridium, Marchantia ). The abundance of insect-pollinated Chamaenerion angustifolium - the most prominent indicator - suggests both its wide distribution in swidden landscapes and a possible link to ground-nesting bee activity, which may aid in identifying buried swiddens. In peat records, the onset of slash-and-burn agriculture was marked by microcharcoal, a decline in late-successional trees, a rise in pioneer taxa, and the appearance of agricultural indicators. Betula dominance in both archives reflects secondary stands typical of long-term swidden use. However, indicators of cultivation and succession were much less pronounced in peat. Our results show that paleosol pollen spectra provide reliable, site-specific evidence of swidden agriculture through local indicator taxa and traces of bee activity, though they tend to overrepresent local vegetation. In contrast, peat and lake records offer broader regional perspectives but detect swidden activity only indirectly. These insights provide a methodological basis for localizing ancient agricultural practices in future archeology and palaeoecology-related studies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".