Data from: The relationship between parasite fitness and host condition in an insect - virus system
Bibliographic record
Abstract
Research in host-parasite evolutionary ecology has demonstrated that environmental variation plays a large role in mediating the outcome of parasite infection. For example, crowding or low food availability can reduce host condition and make them more vulnerable to parasite infection. This observation that poor-condition hosts often suffer more from parasite infection compared to healthy hosts has led to the assumption that parasite productivity is higher in poor-condition hosts. However, the ubiquity of this negative relationship between host condition and parasite fitness is unknown. Moreover, examining the effect of environmental variation on parasite fitness has been largely overlooked in the host-parasite literature. Here we investigate the relationship between parasite fitness and host condition by using a laboratory experiment with the cabbage looper Trichoplusia ni and its viral pathogen, AcMNPV, and by surveying published host-parasite literature. Our experiments demonstrated that virus productivity was positively correlated with host food availability and the literature survey revealed both positive and negative relationships between host condition and parasite fitness. Together these data demonstrate that contrary to previous assumptions, parasite fitness can be positively or negatively correlated with host fitness. We discuss the significance of these findings for host-parasite population biology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".