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Record W1969222909 · doi:10.1086/599086

Condition‐Dependent Sex and the Rate of Adaptation

2009· article· en· W1969222909 on OpenAlexaff
Lilach Hadany, Sarah P. Otto

Bibliographic record

VenueThe American Naturalist · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdaptation (eye)BiologyEvolutionary biologyEcologyZoologyNeuroscience

Abstract

fetched live from OpenAlex

Recent modeling has shown that condition-dependent sex can evolve much more readily than sex that occurs at a uniform rate, even in the face of substantial costs of sex. Specifically, evolution favors genes that cause organisms to allocate more resources to sexual reproduction when they are in poor condition and to asexual reproduction--including increased life span--when they are in good condition. This form of fitness-associated sex (FAS) evolves because modifier genes that promote their own escape from low-fitness genetic backgrounds and that remain longer in high-fitness genetic backgrounds rise in frequency alongside the spread of high-fitness genotypes due to selection. Importantly, FAS does not evolve because it is good for the individual or good for the species but because it is in the selfish interest of modifier genes that promote FAS to move from low- to high-fitness genetic backgrounds. Even though FAS does not evolve for the good of its descendants, we show here that FAS often hastens the rate of adaptation. Ironically, the rate of adaptation is most likely to be accelerated by FAS when sex is costly, because FAS makes it more likely that individuals in poor condition will suffer the costs of sex, improving the efficiency with which less fit alleles are eliminated.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.176

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 head0.005
GPT teacher head0.248
Teacher spread0.243 · 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 designBench or experimental
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

Citations47
Published2009
Admission routes1
Has abstractyes

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