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Record W3203035184 · doi:10.1038/s43247-021-00280-x

Glacial lake outburst floods enhance benthic microbial productivity in perennially ice-covered Lake Untersee (East Antarctica)

2021· article· en· W3203035184 on OpenAlexafffund
Benoit Faucher, Denis Lacelle, Nicole B. Marsh, Liam Jasperse, Ian D. Clark, Dale T. Andersen

Bibliographic record

VenueCommunications Earth & Environment · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaFondation familiale TrottierTawani FoundationNational Aeronautics and Space Administration
KeywordsGlacial periodBenthic zoneOceanographyEcosystemGeologyEnvironmental scienceLast Glacial MaximumCarbon cycleLake ecosystemPhysical geographyHoloceneEcologyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract Benthic ecosystems of perennially ice-covered lakes in Antarctica are highly sensitive to climate-driven changes. Lake Untersee has been in hydrological steady-state for several hundred years with a high pH water column and extremely low levels of dissolved inorganic carbon. Here, we show that glacial lake outburst floods can replenish carbon dioxide-depleted lakes with carbon, enhancing phototrophic activity of the benthic ecosystem. In 2019, a glacial lake outburst flood brought 17.5 million m 3 of water to Lake Untersee, the most substantial reported increase for any surface lake in Antarctica. High-resolution grain-size and carbon isotope analyses of microbial mats suggest that glacial lake outburst floods have occurred periodically over the Holocene and help explain the complex patterns of carbon cycling and sequestration observed in the lake. Our findings suggest that periodic flooding events may provide biological stimuli to other carbon dioxide-depleted Antarctic ecosystems and perhaps even icy lakes on early Mars.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

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 science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.020
GPT teacher head0.259
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2021
Admission routes2
Has abstractyes

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