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THE DYNAMICS OF DIVERSIFICATION IN EVOLVING PSEUDOMONAS POPULATIONS

2006· article· en· W2166921469 on OpenAlexafffund
Rowan D. H. Barrett, Graham Bell

Bibliographic record

VenueEvolution · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyDiversification (marketing strategy)Evolutionary biology

Abstract

fetched live from OpenAlex

Determining the mechanisms that promote the evolution of diversity is a central problem in evolutionary biology. Previous studies have demonstrated that diversification occurs in complex environments and that genotypes specialized on alternative resources can be maintained over short time scales. Here, we describe a selection experiment that has tracked the dynamics of adaptive diversification within selection lines of the asexual bacteria Pseudomonas fluorescens over about 900 generations. We cultured experimental populations from the same two isogenic ancestral strains in simple, single-substrate environments or in complex, four-substrate environments. Following selection we assayed the growth of genotypes from each population on each substrate individually. We estimated mutational heritability, Vm/VE, as 1 x 10(-3) per generation in simple environments and 3 x 10(-3) per generation in complex environments. These values are roughly consistent with estimates reported in other systems. Populations selected in complex environments evolved into genetically diverse communities. Genotypes exhibited greater metabolic differentiation from other genotypes in their own population than to genotypes evolving in other populations, presumably as a result of resource competition. In populations selected in simple environments, little genetic diversity evolved, and genotypes shared very similar phenotypes. Our findings suggest that ecological opportunity provided by environmental complexity plays a major role in the evolution and maintenance of diversity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.307
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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 teacher head, 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

Citations18
Published2006
Admission routes2
Has abstractyes

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