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Record W2157442943 · doi:10.1088/1748-9326/10/3/035002

Can boreal peatlands with pools be net sinks for CO <sub>2</sub> ?

2015· article· en· W2157442943 on OpenAlexafffund
Luc G. Pelletier, Ian B. Strachan, Nigel T. Roulet, Michelle Garneau

Bibliographic record

VenueEnvironmental Research Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaHelsingin Yliopisto
KeywordsPeatEnvironmental scienceEcosystem respirationEddy covarianceSubarctic climateEcosystemCarbon dioxideCarbon sinkBorealSink (geography)Growing seasonCarbon cycleAtmospheric sciencesTemperate climateEnvironmental chemistryEcologyChemistryBiologyGeology

Abstract

fetched live from OpenAlex

Peatland open-water pools, a common feature on temperate to subarctic peatlands, are sources of carbon (C) to the atmosphere but their contribution to the net ecosystem carbon dioxide exchange (NEE-CO _2 ) is poorly known; there is a question as to whether peatlands with pools are smaller sinks of atmospheric C, or even C-neutral, compared to other peatlands. We present growing season NEE-CO _2 measurements using the eddy covariance technique in a peatland with pools. We found the maximum photosynthetic uptake and ecosystem respiration rates at 10 °C to be in the lower range of the published data. The lower total vegetation biomass, due to the presence of pools, reduced CO _2 uptake during day and the autotrophic component of ecosystem respiration. The low CO _2 uptake combined with reduced CO _2 loss resulted in the site being a net sink for CO _2 of a similar magnitude as other northern peatlands despite the inclusion of pools.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.858

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 studies0.0000.001
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 head0.032
GPT teacher head0.281
Teacher spread0.249 · 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

Citations21
Published2015
Admission routes2
Has abstractyes

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