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Record W2039023890 · doi:10.1111/1574-6941.12085

Water mass and depth determine the distribution and diversity of<i>Rhodobacterales</i>in an Arctic marine system

2013· article· en· W2039023890 on OpenAlexafffund
Yunyun Fu, Kimberley Keats, Richard B. Rivkin, Andrew S. Lang

Bibliographic record

VenueFEMS Microbiology Ecology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyBayEcologyPhylogenetic treeZoologyOceanographyGeneGenetics

Abstract

fetched live from OpenAlex

Marine Rhodobacterales are recognized as a widespread, abundant, and metabolically versatile bacterial group in the world's oceans. They also show a nearly universal conservation of the genes for production of gene transfer agents (GTAs), virus-like particles that mediate genetic exchange between cells. It is not yet clear what factors determine the distribution of the various taxonomic subgroups of this order. To address this question, we analyzed the Rhodobacterales communities in 10 seawater samples from northern Baffin Bay collected during September 2008. A conserved gene from the GTA gene cluster was used to characterize the Rhodobacterales community structure. A total of 320 clones from 10 clone libraries were sequenced, and 22 operational taxonomic units representing putative species and 13 clusters representing putative genera were identified. A cluster related to Octadecabacter comprised 59% of total clones from the northern Baffin Bay. Phylogenetic analysis of the clones showed that the Rhodobacterales communities had distinct compositions in the different water masses that were sampled. A change in community structure related to depth was also observed. Therefore, in northern Baffin Bay where two ocean currents meet and mix, the Rhodobacterales community structures were primarily determined by water mass and depth.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.008
GPT teacher head0.185
Teacher spread0.177 · 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.

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

Citations27
Published2013
Admission routes2
Has abstractyes

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