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Record W2061908050 · doi:10.1038/srep03207

Prevalence of psbA-containing cyanobacterial podoviruses in the ocean

2013· article· en· W2061908050 on OpenAlexaff
Qiang Zheng, Nianzhi Jiao, Rui Zhang, Feng Chen, Curtis A. Suttle

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNational High-tech Research and Development ProgramXiamen UniversityUniversity of Hong KongNational Natural Science Foundation of China
KeywordsCladeBiologyMetagenomicsCyanobacteriaGenomeGeneDiversification (marketing strategy)PhotosynthesisENCODEEvolutionary biologyEcologyGeneticsPhylogeneticsBotanyBacteria

Abstract

fetched live from OpenAlex

Podoviruses that infect marine picocyanobacteria are abundant and could play a significant role on regulating host populations due to their specific phage-host relationship. Genome sequencing of cyanophages has unveiled that many marine cyanophages encode certain photosynthetic genes like psbA. It appears that psbA is only present in certain groups of cyanopodovirus isolates. In order to better understand the prevalence of psbA in cyanobacterial podoviruses, we searched the marine metagenomic database (GOS, BATS, HOT and MarineVirome). Our study suggests that 89% of recruited cyanopodovirus scaffolds from the GOS database contained the psbA gene, supporting the ecological relevance of the photosynthesis gene for surface oceanic cyanophages. Diversification between Clade A and B are consistent with recent finding of two major groups of cyanopodoviruses. All the data also shows that Clade B cyanopodoviruses dominate the surface ocean water, while Clade A cyanopodoviruses become more important in the coastal and estuarine environments.

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: Observational
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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