Trematode Infection Prevalence Increases With Snail Richness: Observations From a 4‐Year Study of Snail–Trematode Interactions
Bibliographic record
Abstract
Digenetic trematodes are valuable study organisms for exploring how biodiversity influences disease. In this study, we investigated the relationship between snail richness and trematode infection prevalence using data from a 4-year study (2019-2022) of eight wetland sites in Alberta, Canada. Trematode species were classified as specialists or generalists at the first-intermediate host level, and generalized linear mixed-effects models were employed to assess the relationship between snail richness and overall, generalist, and specialist infection prevalence. The findings indicate that as snail richness increased, there was a significant increase in the overall and generalist infection prevalence. This trend was also noted for specialist infections but was not significant. A notable decline in infection prevalence was observed for all three categories in the final sampling year compared with the first year. Additionally, we found no relationship between snail richness and trematode richness. Trematode and snail-trematode interaction sample completeness and rarefaction analyses indicated that high sample coverage was obtained, but further trematode species remain to be cataloged. We also uncovered interesting one-off infections that could have important implications for disease monitoring and management strategies that rely on snail hosts, emphasizing the need for continued surveillance of host-parasite relationships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".