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Record W2519341718 · doi:10.1038/ismej.2016.112

Ubiquitous marine bacterium inhibits diatom cell division

2016· article· en· W2519341718 on OpenAlexfundno aff
Helena M. van Tol, Shady A. Amin, E. Virginia Armbrust

Bibliographic record

VenueThe ISME Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGordon and Betty Moore Foundation
KeywordsThalassiosira pseudonanaDiatomBiogeochemistryBiologyFrustuleBiogeochemical cyclePlastidBotanyEcologyChloroplastPhytoplanktonBiochemistryGeneNutrient

Abstract

fetched live from OpenAlex

Intricate relationships between microorganisms structure the exchange of molecules between taxa, driving their physiology and evolution. On a global scale, this molecular trade is an integral component of biogeochemical cycling. As important microorganisms in the world's oceans, diatoms and bacteria have a large impact on marine biogeochemistry. Here, we describe antagonistic effects of the globally distributed flavobacterium Croceibacter atlanticus on a phylogenetically diverse group of diatoms. We used the model diatom Thalassiosira pseudonana to study the antagonistic impact in more detail. In co-culture, C. atlanticus attaches to T. pseudonana and inhibits cell division, inducing diatom cells to become larger and increase in chlorophyll a fluorescence. These changes could be explained by an absence of cytokinesis that causes individual T. pseudonana cells to elongate, accumulate more plastids and become polyploid. These morphological changes could benefit C. atlanticus by augmenting the colonizable surface area of the diatom, its photosynthetic capabilities and possibly its metabolic secretions.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.181
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 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

Citations103
Published2016
Admission routes1
Has abstractyes

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