MétaCan
Menu
Back to cohort
Record W2159755681 · doi:10.14430/arctic707

The Annual Carbon Budget for Fen and Forest in a Wetland at Arctic Treeline

2002· article· en· W2159755681 on OpenAlexafffundvenueabout
Wayne R. Rouse, Richard Bello, Alberta D'Souza, Timothy J. Griffis, Peter M. Lafleur

Bibliographic record

VenueARCTIC · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsTrent UniversityYork UniversityMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Nuclear Safety CommissionChurchill Northern Studies Centre
KeywordsTundraEnvironmental sciencePeatSubarctic climateWetlandEcosystemCarbon cycleArcticSoil carbonCarbon fibersCarbon dioxideAtmospheric sciencesEcologyPhysical geographySoil scienceSoil waterGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Three separate research efforts conducted in the same wetland-peatland system in the northern Hudson Bay Lowland near the town of Churchill, Manitoba, allow a comparison of two carbon budget estimates, one derived from long-term growth rates of organic soil and the other based on shorter-term flux measurements. For a tundra fen and an open subarctic forest, calculations of organic soil accumulation or loss over the last half-century indicate that while the fen on average has lost small amounts of carbon from the ecosystem, the adjacent forest has gained larger amounts of atmospheric carbon dioxide. These longer-term data are supported by shorter-term flux measurements and estimates, which also show carbon loss by the fen and carbon uptake by the forest. The shorter-term data indicate that the fen's carbon loss is largely attributable to exceptionally dry years, especially if they are warm. The forest may gain carbon at an increased rate as it matures and during warm growing seasons. Also, the changes in relief of the dynamic hummock-hollow landscape in the fen may inhibit photosynthesis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.942

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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.

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

Citations24
Published2002
Admission routes4
Has abstractyes

Explore more

Same venueARCTICSame topicPeatlands and Wetlands EcologyFrench-language works237,207