Tracing recent large herbivore influence on soil carbon in permafrost and seasonally frozen Arctic ground using lipid biomarkers: a pilot study
Bibliographic record
Abstract
This study investigates the impact of large herbivores on soil organic matter (OM) stability in Arctic permafrost and seasonally frozen ground ecosystems, focusing on the potential preservation effect of grazing. Soil samples were collected from Siberian and Finnish permafrost and nonpermafrost areas and organic carbon content, carbon-to-nitrogen ratio, stable carbon isotopes as well as the content of n-alkanes and n-alcohols were analysed to assess OM stability. The results suggest that grazing activity, particularly in permafrost environments, preserves soil OM by reducing decomposition. Permafrost soils exhibit higher functionalized to nonfunctionalized biomarker ratios, indicating in general better preservation under frozen conditions. While differences in grazing intensities had minor effects, the data also showed variability due to soil heterogeneity, especially in seasonally frozen ground ecosystems. Nevertheless, there are slight trends toward enhanced OM preservation with increasing grazing intensity, especially in permafrost, emphasising the potential role of grazing in locally preserving Arctic soil OM. This pilot study offers initial insights into the impact of large herbivores on OM stability in cold-region ecosystems, suggesting that significant effects may require prolonged, intensive grazing pressure.
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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 | 0.000 |
| 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".