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Record W2311693838 · doi:10.1101/036780

How clonal are bacteria over time?

2016· preprint· en· W2311693838 on OpenAlexaff
B. Jesse Shapiro

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2016
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBiologyHorizontal gene transferGeneticsTraitPopulationRecombinationArchaeaGeneInheritance (genetic algorithm)Evolutionary biologyGene poolMutationGenetic diversityGenome

Abstract

fetched live from OpenAlex

abstract Bacteria and archaea reproduce clonally (vertical descent), but exchange genes by recombination (horizontal transfer). Recombination allows adaptive mutations or genes to spread rapidly within (or even between) species, and reduces the burden of deleterious mutations. Clonality – defined here as the balance between vertical and horizontal inheritance – is therefore a key microbial trait, determining how quickly a population can adapt and the size of its gene pool. Here, I discuss whether clonality varies over time and if it can be considered a stable trait of a given population. I show that, in some cases, clonality is clearly not static. For example, non-clonal (highly recombining) populations can give rise to clonal expansions, often of pathogens. However, an analysis of time-course metagenomic data from a lake suggests that a bacterial population’s past clonality (as measured by its genetic diversity) is a good predictor of its future clonality. Clonality therefore appears to be relatively – but not completely – stable over evolutionary time.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.202
Teacher spread0.195 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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