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Record W4414599570 · doi:10.1139/as-2025-0001

Long-term decomposition of above- and below-ground tissue of two Arctic shrub species

2025· article· en· W4414599570 on OpenAlexafffundvenueabout
Anne Ola, Jérôme Comte, Daniel Fortier, Florent Dominé

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité de MontréalUniversité LavalInstitut National de la Recherche Scientifique
FundersSentinelle Nord, Université LavalNatural Sciences and Engineering Research Council of CanadaInstitut Polaire Français Paul Emile Victor
KeywordsPermafrostShrubAbiotic componentArctic vegetationArcticSubsoilSoil water

Abstract

fetched live from OpenAlex

Arctic soils may become a major source of greenhouse gas emissions, as the climate changes. It is thus crucial to understand decomposition dynamics of permafrost soils, which are influenced by a myriad of abiotic and biotic factors including changing plant communities. Here, litterbags filled either with dried twigs, leaves, and roots of two shrubs ( Salix arctica Pall and encroaching Salix richardsonii Hook) were buried at two different soil depths (0–10 and 10–20 cm) at a site on Bylot Island, Nunavut, Eastern Canadian Arctic, to assess the decomposition (i.e., percent mass loss) of the plant material after a 4-year incubation period. Decomposition was greatest for leaf litter, followed by twigs, and then root tissue of S. richardsonii at shallow depths. Further, root tissue (and to a lesser degree leaf tissue) of S. arctica was more susceptible to decomposition than that of S. richardsonii. Also, mass loss of S. arctica leaf tissue was greater at shallow soil depths compared to deeper soil depths. Hence, shrubification can impact Arctic decomposition dynamics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.302
Teacher spread0.274 · 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.

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 routes4
Has abstractyes

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