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Record W2280618572 · doi:10.1371/journal.pone.0149812

A Diverse Community of Metal(loid) Oxide Respiring Bacteria Is Associated with Tube Worms in the Vicinity of the Juan de Fuca Ridge Black Smoker Field

2016· article· en· W2280618572 on OpenAlexafffund
Chris Maltman, Graham Walter, Vladimir Yurkov

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of VictoriaUniversity of Washington
KeywordsHydrothermal ventBacteriaBiologySulfideEnvironmental chemistryMicrobiologyEcologyHydrothermal circulationChemistryPaleontology

Abstract

fetched live from OpenAlex

Epibiotic bacteria associated with tube worms living in the vicinity of deep sea hydrothermal vents of the Juan de Fuca Ridge in the Pacific Ocean were investigated for the ability to respire anaerobically on tellurite, tellurate, selenite, selenate, metavanadate and orthovanadate as terminal electron acceptors. Out of 107 isolates tested, 106 were capable of respiration on one or more of these oxides, indicating that metal(loid) oxide based respiration is not only much more prevalent in nature than is generally believed, but also is an important mode of energy generation in the habitat. Partial 16S rRNA gene sequencing revealed the bacterial community to be rich and highly diverse, containing many potentially new species. Furthermore, it appears that the worms not only possess a close symbiotic relationship with chemolithotrophic sulfide-oxidizing bacteria, but also with the metal(loid) oxide transformers. Possibly they protect the worms through reduction of the toxic compounds that would otherwise be harmful to the host.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.049
GPT teacher head0.225
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; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations19
Published2016
Admission routes2
Has abstractyes

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