Data from: Sexual antagonism for resistance and tolerance to infection in Drosophila melanogaster
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".