Holocene climate in Northern Urals (Komi Republic, Russia): a multi-proxy approach based on pollen and brGDGTs
Bibliographic record
Abstract
The functioning of the boreal forests of Russia is still little documented and little understood. It is governed by complex mechanisms that link climate, vegetation and disturbances, such as fires or human impact.In this presentation, we aim to provide robust reconstructions of the Holocene climate (temperatures and precipitation) of the Vychegda River basin in the Republic of Komi region, based on two different proxies: pollen assemblages and GDGTs (Glycerol Dialkyl Glycerol Tetraethers). This first study of GDGTs in this area corresponds to a preliminary step for the calibration of this proxy in peats. Higher temperatures and precipitation are recorded between 7000 and 4000 cal. yr BP (mean annual temperatures around 3°C and precipitation between 600 and 700 mm per year). This climatic optimum is in agreement with previous pollen-based climate reconstructions, and climate patterns in the neighboring Russian and Fennoscandia (Komi Republic - previous study-, Arctic Russia, Siberia and Northern Europe, Andreev and Klimanov, 2000; Golubeva, 2008; Seppä et al., 2009a). These results, in conjunction with the reconstruction of fire activity and vegetation dynamics in this region, led to a better understanding of the crossed influences of these factors. In particular, vegetation is mainly controlled by climate during the first part of the Holocene, while a threshold is reached on in fire frequency after 3500 cal. yr BP and this parameter has a greater impact on vegetation than climate. Over the past 600 years, the intensification of human activities led to overexploitation of the forest and an increase in its fire activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".