Land use-induced spillover: a call to action to safeguard environmental, animal, and human health
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 rapid global spread and human health impacts of SARS-CoV-2, the virus that causes COVID-19, show humanity's vulnerability to zoonotic disease pandemics. Although anthropogenic land use change is known to be the major driver of zoonotic pathogen spillover from wildlife to human populations, the scientific underpinnings of land use-induced zoonotic spillover have rarely been investigated from the landscape perspective. We call for interdisciplinary collaborations to advance knowledge on land use implications for zoonotic disease emergence with a view toward informing the decisions needed to protect human health. In particular, we urge a mechanistic focus on the zoonotic pathogen infect-shed-spill-spread cascade to enable protection of landscape immunity-the ecological conditions that reduce the risk of pathogen spillover from reservoir hosts-as a conservation and biosecurity priority. Results are urgently needed to formulate an integrated, holistic set of science-based policy and management measures that effectively and cost-efficiently minimise zoonotic disease risk. We consider opportunities to better institute the necessary scientific collaboration, address primary technical challenges, and advance policy and management issues that warrant particular attention to effectively address health security from local to global scales.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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