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Record W2124094654 · doi:10.1086/345480

Natural Selection in a Bottle

2003· article· en· W2124094654 on OpenAlexaff
David Houle, Locke Rowe

Bibliographic record

VenueThe American Naturalist · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyFecundityNatural selectionSelection (genetic algorithm)PopulationEvolutionary biologyNatural population growthZoologyDrosophila melanogasterEcologyDemographyGeneticsComputer science

Abstract

fetched live from OpenAlex

The study of natural selection in laboratory systems undergoing experimental evolution can provide important insights into the relationship between natural selection and adaptation. We studied selection on the norm of reaction of age at first reproduction in a laboratory population of Drosophila melanogaster. This population had been selected on a discrete generation schedule in the laboratory for more than 600 generations. Using genetically marked strains, we studied development time, size, female fecundity, and viability of flies that began development at different times relative to the initiation of each bottle. Only flies that began development within 30 h of the initiation of the bottle were reliably able to eclose before the next transfer. Theory predicts that flies initiating development around this critical time should decrease size at maturity to ensure eclosion by the 14-d deadline, but late flies are not smaller. This result suggests an unknown constraint on response to selection on age at maturity in this population. Ultimately, laboratory systems provide the best opportunity for the study of natural selection, genetic variation, and evolutionary response in the same population.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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

Citations54
Published2003
Admission routes1
Has abstractyes

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