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Variability in exchange of CO<sub>2</sub> across 12 northern peatland and tundra sites

2009· article· en· W2141884808 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Change Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsCarleton UniversityMcGill UniversityUniversity of LethbridgeTrent University
Fundersnot available
KeywordsTundraEnvironmental scienceEddy covariancePeatPrimary productionEcosystem respirationAtmospheric sciencesEcosystemWetlandGrowing seasonVegetation (pathology)Leaf area indexVapour Pressure DeficitCarbon cycleTerrestrial ecosystemPhysical geographyEcologyGeographyChemistry

Abstract

fetched live from OpenAlex

Abstract Many wetland ecosystems such as peatlands and wet tundra hold large amounts of organic carbon (C) in their soils, and are thus important in the terrestrial C cycle. We have synthesized data on the carbon dioxide (CO 2 ) exchange obtained from eddy covariance measurements from 12 wetland sites, covering 1–7 years at each site, across Europe and North America, ranging from ombrotrophic and minerotrophic peatlands to wet tundra ecosystems, spanning temperate to arctic climate zones. The average summertime net ecosystem exchange of CO 2 (NEE) was highly variable between sites. However, all sites with complete annual datasets, seven in total, acted as annual net sinks for atmospheric CO 2 . To evaluate the influence of gross primary production (GPP) and ecosystem respiration ( R eco ) on NEE, we first removed the artificial correlation emanating from the method of partitioning NEE into GPP and R eco . After this correction neither R eco ( P = 0.162) nor GPP ( P = 0.110) correlated significantly with NEE on an annual basis. Spatial variation in annual and summertime R eco was associated with growing season period, air temperature, growing degree days, normalized difference vegetation index and vapour pressure deficit. GPP showed weaker correlations with environmental variables as compared with R eco , the exception being leaf area index (LAI), which correlated with both GPP and NEE, but not with R eco . Length of growing season period was found to be the most important variable describing the spatial variation in summertime GPP and R eco ; global warming will thus cause these components to increase. Annual GPP and NEE correlated significantly with LAI and pH, thus, in order to predict wetland C exchange, differences in ecosystem structure such as leaf area and biomass as well as nutritional status must be taken into account.

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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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.270
Teacher spread0.250 · 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