The influence of sociodemographic and environmental factors on wildlife carcass submissions in urban areas: Opportunities for increasing equitable and representative wildlife health surveillance
Bibliographic record
Abstract
Wildlife health surveillance is important in rapidly expanding urban areas, where wildlife live in close association with humans and face unique health risks. Urban areas are not homogeneous, and social and environmental factors may affect the distribution of surveillance data we receive from these environments. The Canadian Wildlife Health Cooperative (CWHC) operates a national wildlife surveillance programme that receives carcass submissions for diagnostic evaluation. Our objective was to evaluate sociodemographic and environmental factors associated with CWHC submissions within two cities in Ontario, Canada. Submissions were mapped at two geographic scales and linked with census and environmental data. The results of mixed multivariable Poisson and negative binomial regression analyses suggest that natural (e.g., percent parkland) and anthropogenic environmental (e.g., presence of a zoo) and social variables (e.g., low income) are associated with submissions at both administratively relevant scales. Associations that are common across scales may represent robust intervention points and inform surveillance methodology/messaging. Surveillance data may influence public health policy, wildlife management, and other decision-making regarding the benefits/risks associated with coexistence with wildlife. This study highlights gaps in surveillance methodology that may prevent equal opportunity for participation in wildlife health surveillance and enable equal opportunity to benefit from the associated outputs.
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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 | 0.005 | 0.015 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".