Lessons from the pandemic: Responding to emerging zoonotic viral diseases—a Keystone Symposia report
Bibliographic record
Abstract
The COVID-19 pandemic caught the world largely unprepared, including scientific and policy communities. On April 10-13, 2022, researchers across academia, industry, government, and nonprofit organizations met at the Keystone symposium "Lessons from the Pandemic: Responding to Emerging Zoonotic Viral Diseases" to discuss the successes and challenges of the COVID-19 pandemic and what lessons can be applied moving forward. Speakers focused on experiences not only from the COVID-19 pandemic but also from outbreaks of other pathogens, including the Ebola virus, Lassa virus, and Nipah virus. A general consensus was that investments made during the COVID-19 pandemic in infrastructure, collaborations, laboratory and manufacturing capacity, diagnostics, clinical trial networks, and regulatory enhancements-notably, in low-to-middle income countries-must be maintained and strengthened to enable quick, concerted responses to future threats, especially to zoonotic pathogens.
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.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".