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Record W1646338516 · doi:10.1029/2010gl044018

CO<sub>2</sub> fluxes at northern fens and bogs have opposite responses to inter‐annual fluctuations in water table

2010· article· en· W1646338516 on OpenAlexaffabout
Benjamin N. Sulman, Ankur R. Desai, Nicanor Z. Saliendra, Peter M. Lafleur, Lawrence B. Flanagan, Oliver Sonnentag, D. S. Mackay, Alan Barr, Garth van der Kamp

Bibliographic record

VenueGeophysical Research Letters · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsEnvironment and Climate Change CanadaUniversity of LethbridgeTrent University
FundersU.S. Department of Energy
KeywordsEddy covarianceBogEnvironmental sciencePeatBorealEcosystemEcosystem respirationWater tableAtmospheric sciencesTemperate climateEcohydrologyHydrology (agriculture)EcologySphagnumPhysical geographyClimatologyGeographyGeologyBiologyGroundwater

Abstract

fetched live from OpenAlex

This study compares eddy‐covariance measurements of carbon dioxide fluxes at six northern temperate and boreal peatland sites in Canada and the northern United States of America, representing both bogs and fens. The two peatland types had opposite responses of gross ecosystem photosynthesis (GEP) and ecosystem respiration (ER) to inter‐annual fluctuations in water table level. At fens, wetter conditions were correlated with lower GEP and ER, while at bogs wetter conditions were correlated with higher GEP and ER. We hypothesize that these contrasting responses are due to differences in the relative contributions of vascular plants and mosses. The coherence of our results between sites representing a range of average environmental conditions indicates ecosystem‐scale differences in resilience to hydrological changes that should be taken into account when considering the future of peatland ecosystem services such as carbon sequestration under changing environmental conditions.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.280
Teacher spread0.266 · 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

Citations107
Published2010
Admission routes2
Has abstractyes

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