Emerging infectious disease: An underappreciated area of strategic concern for food security
Bibliographic record
Abstract
Emerging infectious diseases (EIDs) increasingly threaten global food security and public health. Despite technological breakthroughs, we are losing the battle with (re)emerging diseases as treatment costs and production losses rise. A horizon scan of diseases of crops, livestock, seafood and food-borne illness suggests these costs are unsustainable. The paradigm of coevolution between pathogens and particular hosts teaches that emerging diseases occur only when pathogens evolve specific capacities that allow them to move to new hosts. EIDs ought to be rare and unpredictable, so crisis response is the best we can do. Alternatively, the Stockholm Paradigm suggests that the world is full of susceptible but unexposed hosts that pathogens could infect, given the opportunity. Global climate change, globalized trade and travel, urbanization and land-use changes (often associated with biodiversity loss) increase those opportunities, making EID frequent. We can, however, anticipate their arrival in new locations and their behaviour once they have arrived. We can 'find them before they find us', mitigating their impacts. The DAMA (Document, Assess, Monitor, Act) protocol alters the current reactive stance and embodies proactive solutions to anticipate and mitigate the impacts of EID, extending human and material resources and buying time for development of new vaccinations, medications and control measures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".