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THE PROBABILITY THAT BENEFICIAL MUTATIONS ARE LOST IN POPULATIONS WITH PERIODIC BOTTLENECKS

2001· article· en· W1969394745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEvolution · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsChemostatExtinction probabilityBiologyExtinction (optical mineralogy)PopulationMutationPopulation sizeEffective population sizeEvolutionary biologyPopulation bottleneckStatistical physicsStatisticsGeneticsMathematicsGenetic variationAlleleGenePhysicsDemography

Abstract

fetched live from OpenAlex

Population bottlenecks affect the dynamics of evolution, increasing the probability that beneficial mutations will be lost. Recent protocols for the experimental study of evolution involve repeated bottlenecks-when fresh media are inoculated during serial transfer or when chemostat tubes are changed. Unlike population reductions caused by stochastic environmental factors, these bottlenecks occur at known, regular intervals and with a fixed dilution ratio. We derive the ultimate probability of extinction for a beneficial mutation in a periodically bottlenecked population, using both discrete and continuous approaches. We show that both approaches yield the same approximation for extinction probability. From this, we derive an approximate expression for an effective population size.

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.

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 score0.022
Threshold uncertainty score0.987

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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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