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Record W4408427673 · doi:10.5194/egusphere-egu25-11721

Palaeoecological peatland study highlights human impact on the environment of Western Siberia's taiga

2025· preprint· en· W4408427673 on OpenAlexaff
Mariusz Lamentowicz, Michał Słowiński, Katarzyna Marcisz, Sambor Czerwiński, Dominika Łuców

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsCarleton University
Fundersnot available
KeywordsPeatTaigaPhysical geographyBorealGeographyGeologyEarth scienceForestryArchaeology

Abstract

fetched live from OpenAlex

Taiga is one of the world’s largest boreal forest areas that, together with peatlands, span extensive regions of Siberia. Over the past, it has been subject to changes influenced by natural factors and human activity, which utilized available timber and raw materials for their needs. However, Western Siberia's coupled taiga and peatlands history remains insufficiently studied. We lack a comprehensive understanding of the extent to which indigenous people, including the Khanty, influenced taiga through logging, grazing, or controlled fires before the intensive forest exploitation that commenced in the 20th century. Therefore, our research focuses on the palaeoecological reconstruction of environmental changes over the past 1600 years in the Khanty-Mansiysk region (Western Siberia). We employed pollen, testate amoebae, and charcoal from Shapsha bog to reconstruct past dynamics of taiga vegetation and associated changes in the peatland. Our results demonstrate that indigenous communities probably had minimal impact on the natural environment. We also recorded a forest fire at the turn of the 14th and 15th centuries. It may have transformed the ecosystem by raising water levels, enhancing peat accumulation, and fostering peatland growth. Our records indicate that human activities began significantly impacting the environment from the 16th century. The Russian colonization of Siberia is likely responsible for these changes, as it involved organized action of taiga deforestation for purposes such as building settlements, creating trade routes, and developing agriculture. In connection with colonization, Russian settlers took over local areas near the peat bog from the indigenous population and created the town of Shapsha.This research was funded in the framework of the National Science Centre grant No. 2021/41/B/ST10/00060 and INTERACT No. 730938 - PeatHOT project, and within the frame of the IDUB (Excellence Initiative—Research University) programme (003/13/UAM/0007 and 003/13/UAM/0008).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.278
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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