Zero by 2030 and OneHealth: The multidisciplinary challenges of rabies control and elimination
Bibliographic record
Abstract
"Rabies, caused by a negative strand RNA-virus belonging to the \ngenus Lyssavirus (family Rhabdoviridae of the order Mononegavirales), \nremains of global concern [1]. This vaccine-preventable viral zoonotic \ndisease is present in more than 150 countries and territories [2]. Ac- \ncording to the World Health Organization (WHO), rabies is estimated to \ncause ~59,000 human deaths annually, with 95% of cases occurring in \nAfrica and Asia [3,4]. However, rabies still occurs in other regions, such \nas Latin America and the Caribbean [5–8], Central Asia and the Middle \nEast [9,10]. Whilst a number of animals can host the rabies virus, dogs \nare the main source of human rabies deaths, contributing up to 99% of \nall rabies transmissions to humans. Dog-mediated rabies has been \neliminated from Western Europe, Canada, the United States of America \n(USA), Japan and some Latin American countries [11]. Nevertheless, the \nrisk of reintroduction and disease among travellers to risk areas is a \nmatter of concern [12–15]. As occurred with many other communicable \nand non-communicable diseases, the 2020–2022 COVID-19 pandemic \nnegatively impacted the efforts of control and reemergence of rabies in \ncertain countries [7,16,17]. Post-pandemic challenges to enhance con- \ntrol and prevention are multiple and need urgent actions to achieve the \ngoal in eight years by 2030 [16]."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".