Interactions between sources of mortality and the evolution of parasite virulence
Bibliographic record
Abstract
A well-known result from the theory of the evolution of virulence is the prediction that the virulence of a pathogen (i.e. the rate of parasite-induced host mortality) always evolves to higher levels when host background mortality rates increase. This prediction, however, is derived from models that assume that host mortality sources combine additively to determine the overall host mortality rate. In this paper, we suggest that such additivity is probably rare for many host-pathogen systems, and explore how the predictions for the evolution of virulence are altered when interactions between host mortality sources are incorporated into the theory. Our results indicate that if mortality-source interactions are sufficiently strong then the evolutionarily stable level of virulence can actually decrease as the background mortality rate increases. Consequently, a detailed mechanistic description of how parasites and other mortality sources combine to cause host mortality is required before reliable predictions about virulence evolution can be made. Moreover, mortality-source interactions make empirical comparisons of the virulence of different parasites a much more subtle issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".