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Record W4353067203 · doi:10.1080/15230430.2023.2178428

Investigating ten years of warming and enhanced snow depth on nutrient availability and greenhouse gas fluxes in a High Arctic ecosystem

2023· article· en· W4353067203 on OpenAlexafffund
Jacqueline K. Y. Hung, Neal A. Scott, Paul Treitz

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental scienceTundraPermafrostArcticGrowing seasonSnowEcosystemSnowmeltEcosystem respirationArctic vegetationBiogeochemical cycleNutrientGreenhouse gasSoil waterEcologySurface runoffPrimary productionSoil scienceBiology

Abstract

fetched live from OpenAlex

Arctic warming and changing precipitation patterns are altering soil nutrient availability and other processes that control the greenhouse gas balance of high-latitude ecosystems. Changes to these biogeochemical processes will ultimately determine whether the Arctic will enhance or dampen future climate change. At the Cape Bounty Arctic Watershed Observatory, a full-factorial International Tundra Experiment site was established in 2008, allowing for the investigation of ten years of experimental warming and increased snow depth on nutrient availability and trace gas exchange in a mesic heath tundra across two growing seasons (2017 and 2018). Plots with open-top chambers (OTCs) had drier soils (p < .1) that released 1.5 times more carbon dioxide (p < .05), and this effect was enhanced in the drier growing season. Increased snow depth delayed the onset of thaw and active layer development (p < .1) and corresponded with greater nitrous oxide release (p < .05). Our results suggest that decreases to soil moisture will lead to an increase in nitrate availability, soil respiration, and nitrous oxide fluxes. Ultimately, these effects may be moderated by the magnitude of future shifts and interactions between climate variability and ecological responses to permafrost thaw.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.305
Teacher spread0.236 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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