A community analysis approach to parasite transmission in multi-host systems: Assemblages of small mammal prey and Echinococcus multilocularis in an urban area in North America
Bibliographic record
Abstract
Background Echinococcus multilocularis ( Em ) is a parasite with a complex life cycle whose transmission involves a predator-prey interaction. Accidental ingestion of Em eggs by humans may cause alveolar echinococcosis, a potentially fatal disease. Although previous research suggested that the composition of the assemblage of prey species may play a key role in the transmission, the relation between Em presence and the prey assemblages has never been analyzed. Herein, we propose a community analysis approach, based on assemblage similarity statistics, clustering, non-metric dimensional scaling and GLM modelling to analyze the relationships between small mammal assemblages, environmental variables, and the prevalence of Em in intermediate and definitive hosts in an urban area. Results In our study areas within the City of Calgary, Alberta (Canada), we identified three main small mammal assemblages associated with different prevalence of Em , characterized by a different proportion of species known to be good intermediate hosts for Em . As expected, assemblages with higher proportion of species susceptible to Em were observed with higher prevalence of parasite, whereas the total abundance per se of small mammals was not a predictor of transmission likely due to dilution effect. Furthermore, these assemblages were also predicted by simple environmental proxies such as land cover and terrain. Conclusions Our results indicated that the use of a community analysis approach allows for robust characterization of these complex and multivariate relationships, and may offer a promising tool for further understanding of parasite epidemiology in complex multi-host systems. In addition, this analysis indicates that it is possible to predict potential foci of disease risk within urban areas using environmental data commonly available to city planners and land managers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".