Achieving herd immunity in South America
Bibliographic record
Abstract
South America, once an epicenter of COVID-19, has stayed on the road of continued management of the pandemic. The region initially struggled to cope with the pandemic as it experienced spiraling numbers of infections and overwhelmed public health systems. South America has risen in its pandemic response to be the region with the highest global vaccination rate. The region posed a strong vaccination drive, with over 76% of its population fully vaccinated with the initial protocol. South America leveraged its deeply rooted vaccination culture and public health confidence among its population. Herd immunity is an integral concept in population infectious disease management. Attaining herd immunity is presently not feasible with available vaccines, but the high vaccination rate in the region depicts the acceptance of vaccination as a strategy for population protection. The availability of effective transmission-blocking vaccines, the continuous implementation of strategies that will enable the undisrupted supply of the vaccines, equity in access to the vaccines, improved vaccine acceptance, and trust in the vaccination and public health systems will help shepherd the region towards herd immunity. Local vaccine production backed with investment in infrastructure and international collaboration for research and knowledge development will also drive population safety.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".