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Record W6910984765 · doi:10.5061/dryad.t7t5r

Data from: Sexual antagonism for resistance and tolerance to infection in Drosophila melanogaster

2014· dataset· en· W6910984765 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2014
Typedataset
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPleiotropySexual dimorphismDrosophila melanogasterGenetic variationHuman evolutionary geneticsHeritabilityResistance (ecology)Quantitative geneticsDrosophila (subgenus)

Abstract

fetched live from OpenAlex

A critical task in evolutionary genetics is to explain the persistence of heritable variation in fitness-related traits such as immunity. Ecological factors can maintain genetic variation in immunity, but less is known about the role of other factors, such as antagonistic pleiotropy, on immunity. Sexually dimorphic immunity—with females often being more immune-competent—may maintain variation in immunity in dioecious populations. Most eco-immunological studies assess host resistance to parasites rather than the host's ability to maintain fitness during infection (tolerance). Distinguishing between resistance and tolerance is important as they are thought to have markedly different evolutionary and epidemiological outcomes. Few studies have investigated tolerance in animals, and the extent of sexual dimorphism in tolerance is unknown. Using males and females from 50 Drosophila melanogaster genotypes, we investigated possible sources of genetic variation for immunity by assessing both resistance and tolerance to the common bacterial pathogen Pseudomonas aeruginosa. We found evidence of sexual dimorphism and sexual antagonism for resistance and tolerance, and a trade-off between the two traits. Our findings suggest that antagonistic pleiotropy may be a major contributor to variation in immunity, with implications for host–parasite coevolution.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.001
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 head0.028
GPT teacher head0.273
Teacher spread0.245 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2014
Admission routes1
Has abstractyes

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