The influence of infection status and parasitism risk on host dispersal and susceptibility to infection in<i>Drosophila nigrospiracula</i>
Bibliographic record
Abstract
For many organisms, habitat avoidance provides the first line of defence against parasitic infection. Changes in infection status can shift the cost-benefit ratio of remaining in a given habitat vs dispersing. The aim of this study was to test the hypothesis that the propensity to disperse in Drosophila nigrospiracula is mediated by current parasite load and the risk of further infection by an ectoparasitic mite (Macrocheles subbadius). An activity monitor was used to assess dispersal propensity among infected and uninfected flies. The activity level of uninfected females increased threefold upon exposure to a mite, whereas the activity among uninfected males increased by 17-fold in the presence of a questing mite. Among infected flies, the risk of further infection also generated a change in activity, but the magnitude of the response was dependent on host sex. Current infection status influenced the probability of acquiring more parasites due to increased susceptibility to infection with mite load. The probability of acquiring additional mites among males increased more rapidly compared to female flies. Current infection status can potentially determine the risk of further infection, the host propensity and ability to disperse, with consequence for hosts and parasites at the individual, population and species level.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".