Data from: Linking sex differences to the evolution of infectious disease life-histories
Bibliographic record
Abstract
Sex differences in the prevalence, course and severity of infection are widespread, yet the evolutionary consequences of these differences remain unclear. Understanding how male–female differences affect the trajectory of infectious disease requires connecting the contrasting dynamics that pathogens might experience within each sex, to the number of susceptible and infected individuals that are circulating in a population. In this study, we build on theory using genetic covariance functions to link the growth of a pathogen within a host to the evolution and spread of disease between individuals. Using the Daphnia–Pasteuria system as a test case, we show that based on within-host dynamics alone, females seem to be more evolutionary liable for the pathogen, with higher spore loads and greater divergence among pathogen genotypes as infection progresses. Between-host transmission, however, appears to offset the lower performance of a pathogen within a male host, making even subtle differences between the sexes evolutionarily relevant, as long as the selection generated by the between-host dynamics is sufficiently strong. Our model suggests that relatively simple differences in within-host processes occurring in males and females can lead to complex patterns of genetic constraint on pathogen evolution, particularly during an expanding epidemic. This article is part of the theme issue ‘Linking local adaptation with the evolution of sex differences’.
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.037 |
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".