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Record W1654970771 · doi:10.1111/evo.12170

THE CONDITION DEPENDENCY OF FITNESS IN MALES AND FEMALES: THE FITNESS CONSEQUENCES OF JUVENILE DIET ASSESSED IN ENVIRONMENTS DIFFERING IN KEY ADULT RESOURCES

2013· article· en· W1654970771 on OpenAlexafffund
Andrea Elizabeth Zikovitz, Aneil F. Agrawal

Bibliographic record

VenueEvolution · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologySelection (genetic algorithm)Abundance (ecology)Reproductive successJuvenileSexual selectionEcologyKey (lock)ZoologyDemographyPopulation

Abstract

fetched live from OpenAlex

Variation in environmental or genetic quality leads to phenotypic variation in condition, but how much variation in fitness is created by this variation in condition? Using Drosophila melanogaster, we manipulated condition via alternative larval diets and then tested several key factors predicted to influence how much variation in fitness results from differences in condition. Specifically, we were interested in whether male and female fitness are affected equally by condition and whether the strength of selection on condition depends on the abundance of key resources limiting the reproductive output of each sex. We measured selection on condition in alternative assay contexts that varied in the abundance of adult food (a key resource for females) or in the abundance of females (a key resource for males). Overall, selection tended to be stronger on males than females. However, selection on males was weakened when the abundance of their key resource (females) was elevated. Increasing the abundance of the key resource for females (live yeast) elevated their reproductive output as expected but did not change the strength of selection in this sex. Instead, this manipulation increased selection on males, suggesting that this environmental factor indirectly affects selection on males via their interaction with females.

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 score0.289
Threshold uncertainty score0.510

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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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

Citations30
Published2013
Admission routes2
Has abstractyes

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