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Record W2981522320 · doi:10.3390/v11110991

Metagenomic Analysis of Virioplankton from the Pelagic Zone of Lake Baikal

2019· article· en· W2981522320 on OpenAlexaboutno aff
Sergey Potapov, И. В. Тихонова, Andrey Krasnopeev, Мarsel R. Kabilov, А. Е. Тупикин, N.S. Chebunina, Н.А. Жученко, О. И. Белых

Bibliographic record

VenueViruses · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsnot available
FundersSiberian Branch, Russian Academy of SciencesRussian Foundation for Basic Research
KeywordsHuman viromeSiphoviridaePodoviridaeBiologyMetagenomicsEcologyBiotaDetritusBacterioplanktonPhytoplanktonBacteriophageGenetics

Abstract

fetched live from OpenAlex

This study describes two viral communities from the world’s oldest lake, Lake Baikal. For the analysis, we chose under-ice and late spring periods of the year as the most productive for Lake Baikal. These periods show the maximum seasonal biomass of phytoplankton and bacterioplankton, which are targets for viruses, including bacteriophages. At that time, the main group of viruses were tailed bacteriophages of the order Caudovirales that belong to the families Myoviridae, Siphoviridae and Podoviridae. Annotation of functional genes revealed that during the under-ice period, the “Phages, Prophages, Transposable Elements and Plasmids” (27.4%) category represented the bulk of the virome. In the late spring period, it comprised 9.6% of the virome. We assembled contigs by two methods: Separately assembled in each virome or cross-assembled. A comparative analysis of the Baikal viromes with other aquatic environments indicated a distribution pattern by soil, marine and freshwater groups. Viromes of lakes Baikal, Michigan, Erie and Ontario form the joint World’s Largest Lakes clade.

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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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

Citations39
Published2019
Admission routes1
Has abstractyes

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