DISEASE AND MORTALITY IN FREE-RANGING LEPORIDS IN CANADA, 1990–2019: A RETROSPECTIVE STUDY
Bibliographic record
Abstract
Passive surveillance of wildlife disease is a valuable tool for the identification of emerging and changing disease patterns. Free-ranging leporids play an important role in their ecosystem and in the culture and diet of Canadians; however, little is known about their health status and the zoonotic pathogens they may carry. We summarized major causes of mortality and morbidity, as well as incidental infections and lesions, of free-ranging leporids submitted to the Canadian Wildlife Health Cooperative (CWHC) between 1990 and 2019. We identified Canadian leporids as competent hosts for several zoonotic pathogens, most notably Francisella tularensis (20/569; 3.5%). Trauma was the most frequent cause of mortality or morbidity among leporids, accounting for 46.0% of cases submitted to the CWHC, followed by bacterial infections (13.7%) and emaciation (5.1%). Human-mediated mortalities, such as those involving machines (23.7%), were the most common trauma case type, with apparently healthy individuals overrepresented within this mortality group. Harvesters proved to be a valuable resource for the monitoring of diseased and infected animals, as more than half (69.6%) of the animals submitted by this group had an incidental infection or lesion. The results from this study provide a scientific understanding the cause of mortality in free-ranging leporids in Canada with relevance to public health, wildlife biologists, veterinarians, and potential future surveillance programs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".