Parasite prevalence in fecal samples from shelter dogs and cats across the Canadian provinces
Bibliographic record
Abstract
BACKGROUND: In Canada, surveys of enteric parasites in dogs and cats have been reported sporadically over the past 40 years, mostly focusing on a specific region. The present work was performed to determine the current prevalence of various parasites in fecal samples from shelter dogs and cats across the Canadian provinces. METHODS: A total of 1086 dog and 636 cat fecal samples from 26 shelters were analysed using a sugar solution double centrifugal flotation technique. Prevalences (national, regional, provincial, age and parasite-specific), were calculated and compared using the Fisher-Exact test. A multiplex PCR was performed to distinguish Taenia spp, Echinococcus granulosus and E. multilocularis on samples positive for taeniid eggs. RESULTS: Overall, 33.9% of dogs and 31.8% of cats were positive for at least one parasite. Toxocara canis and T. cati were the most prevalent parasite present in fecal samples followed by Cystoisospora spp. Prevalence in dogs was similar across the Atlantic, East, West and Pacific regions, while prevalence in cats varied regionally. Eggs of E. granulosus/E. canadensis were detected in samples from dogs from BC, AB, and ON. CONCLUSIONS: Data from this study will help in the development of strategies, based on the level of risk per geographic location for the prevention and response to these parasites in pets and free-roaming and shelter animals in Canada.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".