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Record W2033147097 · doi:10.1029/2006jg000358

Metabolic activity and diversity of cryoconites in the Taylor Valley, Antarctica

2007· article· en· W2033147097 on OpenAlexaboutno aff
Christine M. Foreman, Birgit Sattler, Jill A. Mikucki, Dorota L. Porazinska, John C. Priscu

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsBiogeochemical cycleBiomass (ecology)SedimentGlacierEnvironmental chemistryTotal organic carbonGeologyOceanographyEnvironmental scienceChemistryPaleontology

Abstract

fetched live from OpenAlex

Metabolic activity and biogeochemical diversity within cryoconites from the Canada, Commonwealth, Howard, and Hughes glaciers in the McMurdo Dry Valleys revealed the presence of a productive microbial refuge in this polar desert ecosystem. Fluorescent in situ hybridization showed a high percentage of Cytophaga ‐ Flavobacteria cells in cryoconite sediments (87.2%), while β ‐ Proteobacterial cells dominated the ice overlying the sediment layer (54.2%). The biomass of bacterial cells in the sediments was also greater (4.82 μ gC ml −1 ) than that in the overlying ice (0.18 μ gC ml −1 ) and was related to bacterial productivity (on the basis of thymidine incorporation), which ranged from 36 ng C l −1 d −1 in the overlying ice to 3329 ng C l −1 d −1 in the sediment‐containing layers. Bacteria within both the sediments and overlying ice were able to actively incorporate and respire radio‐labeled glucose, as well as 17 other dissolved organic carbon compounds. The cryoconites in the Taylor Valley support an active, diverse assemblage of organisms despite the fact that they may remain sealed from the atmosphere for decades. Given the density of the cryoconites in the dry valleys (∼4–6% of ablation zone surfaces), flushing of the cryoconites during warm years could provide a vital nutrient and organic carbon source to the surrounding polar desert.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.327
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations151
Published2007
Admission routes1
Has abstractyes

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