MétaCan
Menu
Back to cohort

Winter‐to‐summer changes in the composition and single‐cell activity of near‐surface Arctic prokaryotes

2008· article· en· W2145215021 on OpenAlexfundno aff
Laura Alonso‐Sáez, Olga Sánchez, Josep M. Gasol, Vanessa Balagué, Carlos Pedrós‐Alió

Bibliographic record

VenueEnvironmental Microbiology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersGeneralitat de CatalunyaNatural Sciences and Engineering Research Council of CanadaUniversity of Warwick
KeywordsAlphaproteobacteriaBiologyGammaproteobacteriaBetaproteobacteriaBacteroidetesArchaeaBacteriaArcticRoseobacterBotanyEcologyBiochemistry16S ribosomal RNAActinobacteria

Abstract

fetched live from OpenAlex

We collected surface samples in Franklin Bay (Western Arctic) from ice-covered to ice-free conditions, to determine seasonal changes in the identity and in situ activity of the prokaryotic assemblages. Catalysed reported fluorescence in situ hybridization was used to quantify the abundance of different groups, and combined with microautoradiography to determine the fraction of active cells taking up three substrates: glucose, amino acids and ATP. In surface waters, Archaea accounted for 16% of the total cell count in winter, but decreased to almost undetectable levels in summer, when Bacteria made up 97% of the total cell count. Alphaproteobacteria were the most abundant group followed by Bacteroidetes (average of 34% and 14% of total cell counts respectively). Some bacterial groups appearing in low abundances (< 10% of total cell counts), such as Betaproteobacteria, Roseobacter and Gammaproteobacteria, showed a high percentage of active cells. By contrast, more abundant groups, such as SAR11 or Bacteroidetes, had a lower percentage of active cells in the uptake of the substrates tested. Archaea showed low heterotrophic activity throughout the year. In comparison with temperate oceans, the percentage of active Bacteria in the uptake of the substrates was relatively high, even during the winter season.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.995

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.201
Teacher spread0.187 · 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 designBench or experimental
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

Citations156
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental MicrobiologySame topicMicrobial Community Ecology and PhysiologyFrench-language works237,207