Canadian wildlife health surveillance—patterns, challenges and opportunities identified by a scoping review
Bibliographic record
Abstract
The protection and promotion of healthy wildlife populations is emerging as a shared goal among stakeholders in the face of unprecedented environmental threats. Accordingly, there are growing demands for the generation of actionable wildlife health information. Wildlife health surveillance is a connected system of knowledge that generates data on a range of factors that influence health. Canada recently approved the Pan-Canadian Approach to Wildlife Health that describes challenges facing wildlife health programs and provides a path forward for modernizing our approach. This scoping review was undertaken to describe the range of peer-reviewed Canadian wildlife health surveillance literature within the context of the challenges facing wildlife health programs and to provide a quantitative synthesis of evidence to establish baselines, identify gaps, and inform areas for growth. This review describes patterns related to species, location, authorship/funding, objectives, and methodology. Five areas are identified that have the potential to propel the field of wildlife health: representativeness, expanded/diversified collaboration, community engagement, harmonization, and a shift to a solutions-focused and One Health mindset. This scoping review provides a synopsis of 10 years of Canadian wildlife health surveillance, challenges us to envision the future of successful wildlife health surveillance, and provides a benchmark from which we can measure change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.031 | 0.060 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".