USING WILDLIFE REHABILITATION AND POSTMORTEM DATA TO IDENTIFY KEY CAUSES OF MORBIDITY AND MORTALITY IMPACTING THE HEALTH AND WELFARE OF FREE-LIVING WILD ANIMALS IN CANADA
Bibliographic record
Abstract
The health and welfare of wild animals are of increasing concern, yet there are very few large-scale data syntheses examining how causes of wildlife morbidity and mortality vary across time, space, and taxa. Records for 18,540 animals submitted to the Canadian Wildlife Health Cooperative (CWHC) (2009-19) and 144,846 animals admitted to 19 wildlife rehabilitation centers (WRCs) (2015-19) were evaluated to 1) identify the main causes of morbidity and mortality for Canadian wildlife and 2) assess the utility and complementarity of these two data sources to further our understanding of wildlife health. The CWHC cases (mortality) were examined by pathologists and grouped by the presence or absence of five diagnostic categories: trauma, emaciation, infection or inflammation, toxicity, and other. These CWHC animals were also classified as "killed due to real or perceived human-wildlife conflict" based on finder history. The WRC admissions were categorized by health issue (according to intake records) and based on reported or observed situational reasons for admission: parental loss, unsafe or unsuitable location, nest or habitat disturbance, illegal possession, and abnormal behavior. For both datasets, the main reason for submission or admission was trauma (44 and 48%, respectively), especially vehicle collisions (7 and 11%) and window or building strikes (5 and 7%). Many other WRC admissions were due to parental loss (28%), cat attacks (6%), and immature animals being found in unsafe or unsuitable locations (6%). Most other CWHC mortalities were caused by infections (27%) and emaciation (23%). Relatively few birds, amphibians, and reptiles submitted to CWHC were killed due to human-wildlife conflict, but 22% of mammals were killed for this reason, highlighting the taxonomic differences in the perceived threat of wildlife to finders, and therefore their response. Together, these data sources highlight key issues impacting the health and welfare of wild 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".