Essential contributions of wildlife health surveillance to the United Nations Sustainable Development Goals
Bibliographic record
Abstract
In response to the urgent need to protect the environment, economy, and society, the United Nations (UN) developed the Sustainable Development Goals (SDG) in 2015. The Sustainable Development Goals expand on the Millennium Development Goals as part of the UN’s broader effort to address global development needs. These goals aim to end poverty and other deprivations by improving health and education, reducing inequality, addressing climate change, and preserving oceans and forests. Protecting wildlife health, which is intrinsically linked to ecosystem health, can enhance socio-ecological resilience and support a sustainable future. Wildlife health surveillance is a vital tool for monitoring and mitigating health hazards and disease risks across species and ecosystems, contributing significantly to human, animal, and environmental health. We have identified comprehensive ways in which wildlife health surveillance activities are essential to achieving the Sustainable Development Goals, particularly: Zero Hunger (SDG 2), Good Health and Well-Being (SDG 3), Clean Water and Sanitation (SDG 6), Decent Work and Economic Growth (SDG 8), Responsible Consumption and Production (SDG 12), Climate Action (SDG 13), Life Below Water (SDG 14), Life on Land (SDG 15), and Partnerships for the Goals (SDG 17). We highlight the importance of investing in and optimizing wildlife health surveillance to advance the global sustainability agenda. Sustainable surveillance systems tailored to local contexts are key to achieving the SDGs.
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".