Bibliographic record
Abstract
The study of social evolution and virulence in parasites is concerned with fitness consequences of trade-offs between parasite life history traits and interactions between parasite species and/or genotypes with their hosts. I develop our understanding of social evolution and virulence in parasites in several ways. (1) I review empirical evidence for the fundamental predictions of virulence-transmission trade-off theory and demonstrate that the fit between theory and data is primarily qualitative rather than quantitative; that parasites differ in their degree of host generalism, and this is likely to impact virulence in four ways. (2) I take a comparative approach to examine the underlying causes of an observed statistical variation in the size of parasite infectious doses across taxa, revealing that mechanisms used by parasites to infect hosts are able to explain variation in both infectious dose and virulence. (3) I formally compare data on human pathogens to explain variation in virulence across taxa, revealing that immune subversion and not growth rate, explains variation in virulence. This allows me to predict that immune subverters and not fast growing parasites are likely to cause the most virulent clinical infections. (4) Using bacteria and their naturally infecting viruses (bacteriophage), I take an experimental approach to investigate the consequences of coinfection for parasite life history traits, and find that viruses cultured under a mix of single infections and coinfections evolved plasticity; they killed hosts more rapidly when coinfecting, and this resulted in high fitness under both single infection and coinfection conditions. (5) I experimentally investigate how selection within and between hosts and patches of hosts affects the fitness and virulence of populations of these viruses. I find that limited host availability favours virulent, faster killing parasites with reduced transmission; suggesting high, rather than low, virulence may be common in spatially structured host-parasite communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".