Antimicrobial Resistance Must Be Included in the Pandemic Instrument to Ensure Future Global Pandemic Readiness
Bibliographic record
Abstract
Governments can practically and efficiently address zoonoses and AMR -- within the text of the new pandemic instrument. We map the overlaps between the efforts needed to address both pandemic threats, including (a) equitable access to medical countermeasures, (b) globally integrated One Health surveillance and monitoring systems, (c) increased technical and laboratory capacity in low- and middle-income countries, and (d) a regulatory framework governing the stewardship of antimicrobials. By outlining potential dual-purpose provisions that could be included in a pandemic instrument, we argue that addressing AMR in the pandemic instrument is practicable, the most effective use of limited time and resources, and provides the best opportunity for future global pandemic readiness.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".