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Record W4403289537 · doi:10.1139/as-2023-0033

Tracing recent large herbivore influence on soil carbon in permafrost and seasonally frozen Arctic ground using lipid biomarkers: a pilot study

2024· article· en· W4403289537 on OpenAlexvenueno aff
Torben Windirsch, Kai Mangelsdorf, Guido Grosse, Juliane Wolter, Loeka L. Jongejans, Jens Strauß

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsPermafrostArcticEnvironmental scienceTracingSoil carbonHerbivoreThe arcticEcologyOceanographySoil scienceSoil waterGeologyBiologyComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.061
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.286
Teacher spread0.231 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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