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Record W2925023544 · doi:10.1088/1748-9326/ab11ec

Recent peat and carbon accumulation following the Little Ice Age in northwestern Québec, Canada

2019· article· en· W2925023544 on OpenAlexafffundabout
Sanna Piilo, Hui Zhang, Michelle Garneau, Angela Gallego‐Sala, Matthew J. Amesbury, Minna Väliranta

Bibliographic record

VenueEnvironmental Research Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAcademy of FinlandNatural Environment Research CouncilSight Research UK
KeywordsPeatSubarctic climateBorealEnvironmental sciencePhysical geographyBogCarbon sinkCarbon cycleCarbon fibersVegetation (pathology)MacrofossilClimate changeEcosystemOmbrotrophicAtmospheric sciencesClimatologyGeologyEcologyGeographyHoloceneOceanographyBiology

Abstract

fetched live from OpenAlex

Abstract Peatland ecosystems are important carbon sinks, but also release carbon back to the atmosphere as carbon dioxide and methane. Peatlands therefore play an essential role in the global carbon cycle. However, the response of high-latitude peatlands to ongoing climate change is still not fully understood. In this study, we used plant macrofossils and peat property analyses as proxies to document changes in vegetation and peat and carbon accumulation after the Little Ice Age. Results from 12 peat monoliths collected in high-boreal and low-subarctic regions in northwestern Québec, Canada, suggest high carbon accumulation rates for the recent past (post AD 1970s). Successional changes in plant assemblages were asynchronous within the cores in the southernmost region, but more consistent in the northern region. Average apparent recent carbon accumulation rates varied between 50.7 and 149.1 g C m −2 yr −1 with the northernmost study region showing higher values. The variation in vegetation records and peat properties found within samples taken from the same sites and amongst cores taken from different regions highlights the need to investigate multiple records from each peatland, but also from different peatlands within one region.

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 score0.249
Threshold uncertainty score0.572

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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

Citations37
Published2019
Admission routes3
Has abstractyes

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