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Record W2767135886 · doi:10.1657/aaar0016-064

Respiration from Soil and Ground Cover Vegetation Under Tundra Shrubs

2017· article· en· W2767135886 on OpenAlexafffundabout
Ge Le, Peter M. Lafleur, Elyn Humphreys

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCarleton UniversityTrent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTundraShrubEnvironmental scienceSoil respirationVegetation (pathology)Soil carbonAbundance (ecology)EcosystemEcologyAtmospheric sciencesAgronomySoil waterSoil scienceBiologyGeology

Abstract

fetched live from OpenAlex

Atmospheric warming is expected to cause shifts in arctic tundra vegetation composition, especially in the abundance and distribution of shrub species. Greater shrub abundance will impact the carbon exchanges between tundra ecosystems and the atmosphere, including ecosystem respiration. Here, total respiration under the shrub canopy (RT) and its components soil respiration (RS) and respiration from the ground cover vegetation (RG) were investigated at three tundra sites in the Canadian Low Arctic with varying shrub coverage. Seasonal RT and RS mean values were significantly greater (P < 0.05) at the site with greatest shrub abundance; mean values were 3.70 and 3.22 μmol m-2 s-1, respectively. Mean RG did not differ among sites; mean values ranged from 0.45 to 0.52 μmol m-2 s-1. Soil temperature exerted a stronger control on RT and RS compared to soil moisture. Differences in RT and RS among sites were attributed to differences in soil properties, such as soil total N content and bulk density. These findings suggest that belowground sources of respired carbon dioxide in Low Arctic tundra may vary with long-term shrub expansion as soil microclimate conditions and physiochemical properties adjust to changes in shrub coverage.

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)0.001
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 score0.068
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.131
GPT teacher head0.346
Teacher spread0.215 · 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

Citations13
Published2017
Admission routes3
Has abstractyes

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