Animal board invited review: Risks of zoonotic disease emergence at the interface of wildlife and livestock systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ongoing coronavirus disease 19s pandemic has yet again demonstrated the importance of the human-animal interface in the emergence of zoonotic diseases, and in particular the role of wildlife and livestock species as potential hosts and virus reservoirs. As most diseases emerge out of the human-animal interface, a better understanding of the specific drivers and mechanisms involved is crucial to prepare for future disease outbreaks. Interactions between wildlife and livestock systems contribute to the emergence of zoonotic diseases, especially in the face of globalization, habitat fragmentation and destruction and climate change. As several groups of viruses and bacteria are more likely to emerge, we focus on pathogenic viruses of the Bunyavirales, Coronaviridae, Flaviviridae, Orthomyxoviridae, and Paramyxoviridae, as well as bacterial species including Mycobacterium sp., Brucella sp., Bacillus anthracis and Coxiella burnetii. Noteworthy, it was difficult to predict the drivers of disease emergence in the past, even for well-known pathogens. Thus, an improved surveillance in hotspot areas and the availability of fast, effective, and adaptable control measures would definitely contribute to preparedness. We here propose strategies to mitigate the risk of emergence and/or re-emergence of prioritized pathogens to prevent future epidemics.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it