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Bacteria beneath the West Antarctic Ice Sheet

2008· article· en· W2048795567 on OpenAlexaff
Brian Lanoil, Mark Skidmore, John C. Priscu, Sukkyun Han, W. Foo, S. W. Vogel, Sławek Tulaczyk, Hermann Engelhardt

Bibliographic record

VenueEnvironmental Microbiology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversity of Alberta
FundersOffice of Polar ProgramsNational Science Foundation
KeywordsAntarctic ice sheetIce sheetArcticBiologySedimentEcosystemIce streamOceanographyCryosphereSea iceGeologyEcologyPaleontology

Abstract

fetched live from OpenAlex

Subglacial environments, particularly those that lie beneath polar ice sheets, are beginning to be recognized as an important part of Earth's biosphere. However, except for indirect indications of microbial assemblages in subglacial Lake Vostok, Antarctica, no sub-ice sheet environments have been shown to support microbial ecosystems. Here we report 16S rRNA gene and isolate diversity in sediments collected from beneath the Kamb Ice Stream, West Antarctic Ice Sheet and stored for 15 months at 4 degrees C. This is the first report of microbes in samples from the sediment environment beneath the Antarctic Ice Sheet. The cells were abundant ( approximately 10(7) cells g(-1)) but displayed low diversity (only five phylotypes), likely as a result of enrichment during storage. Isolates were cold tolerant and the 16S rRNA gene diversity was a simplified version of that found in subglacial alpine and Arctic sediments and water. Although in situ cell abundance and the extent of wet sediments beneath the Antarctic ice sheet can only be roughly extrapolated on the basis of this sample, it is clear that the subglacial ecosystem contains a significant and previously unrecognized pool of microbial cells and associated organic carbon that could potentially have significant implications for global geochemical processes.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.986

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0300.015

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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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

Citations172
Published2008
Admission routes1
Has abstractyes

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