Global Environmental Change and Emerging Infectious Diseases
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 prediction of emerging infectious diseases (EIDs) and the avoidance of their tremendous social and economic costs is contingent on the identification of their most likely drivers. It is argued that the drivers of global environmental change (and climate change as both a driver and an impact) are often the drivers of EIDs; and that the two overlap to such a strong degree that targeting these drivers is sound epidemiological policy. Several drivers overlap with the leading causes of biodiversity loss, providing opportunities for health and biodiversity sectors to generate synergies at local and global levels. This chapter provides a primer on EID ecology, reviews underlying drivers and mechanisms that facilitate pathogen spillover and spread, provides suggested policy and practice-based actions toward the prevention of EIDs in the context of environmental change, and identifies knowledge gaps for the purpose of further research.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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