Renewed Global Partnerships and Redesigned Roadmaps for Rabies Prevention and Control
Bibliographic record
Abstract
Canine rabies, responsible for most human rabies deaths, is a serious global public health concern. This zoonosis is entirely preventable, but by focusing solely upon rabies prevention in humans, this "incurable wound" persists at high costs. Although preventing human deaths through canine rabies elimination is feasible, dog rabies control is often neglected, because dogs are not considered typical economic commodities by the animal health sector. Here, we demonstrate that the responsibility of managing rabies falls upon multiple sectors, that a truly integrated approach is the key to rabies elimination, and that considerable progress has been made to this effect. Achievements include the construction of global rabies networks and organizational partnerships; development of road maps, operational toolkits, and a blueprint for rabies prevention and control; and opportunities for scaling up and replication of successful programs. Progress must continue towards overcoming the remaining challenges preventing the ultimate goal of rabies elimination.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".