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Prokaryotic diversity of arctic ice shelf microbial mats

2008· article· en· W1982845781 on OpenAlexafffundabout
Eric Bottos, Warwick F. Vincent, Charles W. Greer, Lyle G. Whyte

Bibliographic record

VenueEnvironmental Microbiology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsBiotechnology Research InstituteUniversité LavalCenter for Northern StudiesMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaArcticNet
KeywordsBiologyLibraryBacteroidetesArcticProteobacteriaExtreme environmentBiodiversityEcology16S ribosomal RNAActinobacteriaMetagenomicsThaumarchaeotaPhylumPhylotypeMicrobial matFirmicutesBacteriaCyanobacteriaGenePaleontology

Abstract

fetched live from OpenAlex

The prokaryotic diversity and respiratory activity of microbial mat communities on the Markham Ice Shelf and Ward Hunt Ice Shelf in the Canadian high Arctic were analysed. All heterotrophic isolates and > 95% of bacterial 16S rRNA gene clone library sequences from both ice shelves grouped within the phyla Bacteroidetes, Proteobacteria and Actinobacteria. Clone library analyses showed that the bacterial communities were diverse and varied significantly between the two ice shelves, with the Markham library having a higher estimated diversity (Chao1 = 243; 105 operational taxonomic units observed in 189 clones) than the Ward Hunt library (Chao1 = 106; 52 operational taxonomic units observed in 128 clones). Archaeal 16S rRNA gene clone libraries from both ice shelves were dominated by a single Euryarchaeota sequence, which appears to represent a novel phylotype. Analyses of community activity by radiorespiration assays detected metabolism in mat samples from both ice shelves at temperatures as low as -10 degrees C. These findings provide the first insight into the prokaryotic biodiversity of Arctic ice shelf communities and underscore the importance of these cryo-ecosystems as a rich source of microbiota that are adapted to extreme cold.

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 score0.561
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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.004

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 head0.010
GPT teacher head0.186
Teacher spread0.176 · 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

Citations70
Published2008
Admission routes3
Has abstractyes

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