A meta-analysis highlights the idiosyncratic nature of tradeoffs in laboratory models of virus evolution
Bibliographic record
Abstract
Different theoretical frameworks have been invoked to guide the study of virus evolution. Three of the more prominent ones are (i) the evolution of virulence, (ii) life history theory, and (iii) the generalism-specialism dichotomy. All involve purported tradeoffs between traits that define the evolvability and constraint of virus-associated phenotypes. However, as popular as these frameworks are, there is a surprising paucity of direct laboratory tests of the frameworks that support their utility as broadly applicable theoretical pillars that can guide our understanding of disease evolution. In this study, we conduct a meta-analysis of direct experimental evidence for these three frameworks across several widely studied virus-host systems: plant viruses, fungal viruses, animal viruses, and bacteriophages. We extracted 60 datasets from 28 studies and found a range of relationships between traits in different analysis categories (e.g., frameworks, virus-host systems). Our work demonstrates that direct evidence for relationships between traits is highly idiosyncratic and specific to the host-virus system and theoretical framework. Consequently, scientists researching viral pathogens from different taxonomic groups might reconsider their allegiance to these canons as the basis for expectation, explanation, or prediction. Future efforts could benefit from consistent definitions, and from developing frameworks that are compatible with the evidence and apply to particular biological and ecological contexts.
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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 | 0.054 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".