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Record W2410132414 · doi:10.1093/femsec/fiw117

Temperature effects on net greenhouse gas production and bacterial communities in arctic thaw ponds

2016· article· en· W2410132414 on OpenAlexaff
Karita Negandhi, Isabelle Laurion, Connie Lovejoy

Bibliographic record

VenueFEMS Microbiology Ecology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern StudiesInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBiologyAcidobacteriaActinobacteriaArcticGreenhouse gasEcologyMicrobial population biology16S ribosomal RNABacteria

Abstract

fetched live from OpenAlex

One consequence of High Arctic permafrost thawing is the formation of small ponds, which release greenhouse gases (GHG) from stored carbon through microbial activity. Under a climate with higher summer air temperatures and longer ice-free seasons, sediments of shallow ponds are likely to become warmer, which could influence enzyme kinetics or select for less cryophilic microbes. There is little data on the direct temperature effects on GHG production and consumption or on microbial communities' composition in Arctic ponds. We investigated GHG production over 16 days at 4°C and 9°C in sediments collected from four thaw ponds. Consistent with an enzymatic response, production rates of CO2 and CH4 were significantly greater at higher temperatures, with Q10 varying from 1.2 to 2.5. The bacterial community composition from one pond was followed through the incubation by targeting the V6-V8 variable regions of the 16S rRNA gene and 16S rRNA. Several rare taxa detected from rRNA accounted for significant community compositional changes. At the higher temperature, the relative community contribution from Bacteroidetes decreased by 15% with compensating increases in Betaproteobacteria, Alphaproteobacteria, Firmicutes, Acidobacteria, Verrucomicrobia and Actinobacteria. The increase in experimental GHG production accompanied by changes in community indicates an additional factor to consider in sediment environments when evaluating future climate scenarios.

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 score0.293
Threshold uncertainty score0.997

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)0.0040.000

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 head0.014
GPT teacher head0.205
Teacher spread0.191 · 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

Citations56
Published2016
Admission routes1
Has abstractyes

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