An examination of the manipulation hypothesis to explain prevalence of <i>Parelaphostrongylus tenuis</i> in gastropod intermediate host populations
Bibliographic record
Abstract
There are many examples of parasites that manipulate intermediate host behaviour facilitating transmission to the definitive host. We examined this hypothesis as a possible explanation for the observed prevalence of Parelaphostrongylus tenuis, a nematode parasite of white-tailed deer (Odocoileus virginianus) in eastern North America. The prevalence of this parasite in deer populations is typically high compared with its prevalence in its gastropod intermediate hosts. It is unclear how high levels of prevalence are maintained, given that the transmission of this parasite to deer is considered to occur by the accidental ingestion of infected gastropods. We tested the manipulation hypothesis by comparing the vertical climbing behaviour of infected and uninfected individuals of a known intermediate host species, Mesodon sayanus. Snails climbed higher at dawn than during the rest of the day and adults climbed higher than juveniles, but no differences were found between infected and uninfected snails. These results do not explain the observed levels of prevalence, but they improve our understanding of the transmission of P. tenuis, specifically when examining the relative importance of different gastropod intermediate hosts. We review competing hypotheses that might account for the apparent discrepancy in levels of prevalence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".