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Record W2109070719 · doi:10.1371/journal.pone.0078204

Small Thaw Ponds: An Unaccounted Source of Methane in the Canadian High Arctic

2013· article· en· W2109070719 on OpenAlexafffundabout
Karita Negandhi, Isabelle Laurion, Michael J. Whiticar, Pierre E. Galand, Xiaomei Xu, Connie Lovejoy

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaArcticNetParks Canada
KeywordsMethanogenesisMethaneMethanotrophPermafrostEnvironmental scienceEnvironmental chemistryTundraIsotopes of carbonPeatArchaeaArcticCarbon dioxideCarbon fibersThermokarstEcologyBiologyTotal organic carbonChemistryAnaerobic oxidation of methane

Abstract

fetched live from OpenAlex

Thawing permafrost in the Canadian Arctic tundra leads to peat erosion and slumping in narrow and shallow runnel ponds that surround more commonly studied polygonal ponds. Here we compared the methane production between runnel and polygonal ponds using stable isotope ratios, ¹⁴C signatures, and investigated potential methanogenic communities through high-throughput sequencing archaeal 16S rRNA genes. We found that runnel ponds had significantly higher methane and carbon dioxide emissions, produced from a slightly larger fraction of old carbon, compared to polygonal ponds. The methane stable isotopic signature indicated production through acetoclastic methanogenesis, but gene signatures from acetoclastic and hydrogenotrophic methanogenic Archaea were detected in both polygonal and runnel ponds. We conclude that runnel ponds represent a source of methane from potentially older C, and that they contain methanogenic communities able to use diverse sources of carbon, increasing the risk of augmented methane release under a warmer climate.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.982

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)0.0190.000

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 head0.091
GPT teacher head0.214
Teacher spread0.123 · 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.

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

Citations96
Published2013
Admission routes3
Has abstractyes

Explore more

Same venuePLoS ONESame topicClimate change and permafrostFrench-language works237,207