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Record W3122520681 · doi:10.1177/0959683620988031

Carbon accumulation in peatlands along a boreal to subarctic transect in eastern Canada

2021· article· en· W3122520681 on OpenAlexafffundabout
Guillaume Primeau, Michelle Garneau

Bibliographic record

VenueThe Holocene · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeatSubarctic climateBogBorealTransectPhysical geographyEnvironmental scienceClimate changeWatershedGeologyEcologyGeographyOceanographyBiology

Abstract

fetched live from OpenAlex

In this study, we investigated the links between peat carbon accumulation and past ecological and hydrological conditions in three peatlands (Bouleau, Mista, Auassat) which developed along a South-North transect within a watershed encompassing the boreal and subarctic domain in Eastern Canada. Peatland development and long-term apparent rates of carbon accumulation (LORCA) were asynchronous in the watershed, suggesting an influence of both latitude and topography (altitude) on the length of the growing season (GGD 0 ). Results show that peat initiation within the three peatlands (respectively ca. 9070, 8400, and 6270 cal BP) was delayed after the deglaciation and that LORCA (respectively 35.5, 15.4, and 9.0 g C m −2 yr −1 ) decreased from South to North. Peatland development and fen to bog transitions were found to be almost synchronous for the two southernmost sites. The fen to bog transition in the northernmost subarctic site was delayed until the 20th century, owing to the less favorable climatic conditions. This suggests that recent warming has extended the length of the growing season and increased Sphagnum growth enough to potentially influence an ecosystem state-shift as observed in other Subarctic regions of eastern Canada.

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 score0.228
Threshold uncertainty score0.267

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations41
Published2021
Admission routes3
Has abstractyes

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