Adopting a Global AMR Target within the Pandemic Instrument Will Act as a Catalyst for Action
Bibliographic record
Abstract
Abstract Ensuring that life-saving antimicrobials remain available as effective treatment options in the face of rapidly rising levels of antimicrobial resistance will require a massive and coordinated global effort. Setting a collective direction for progress is the first step towards aligning global efforts on AMR. This process would be greatly accelerated by adopting a unifying global target — a well-defined global target that unites all countries and sectors. The proposed pandemic instrument — with its focus on prevention, preparedness and response — represents an ideal opportunity to develop and adopt a unifying global target that catalyzes global action on AMR. We propose three key characteristics of a unifying global target for AMR that — if embedded within the pandemic preparedness instrument — could rally public support, funding, and political commitment commensurate with the scale of the AMR challenge.
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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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".