Bibliographic record
Abstract
Every year, more than 55,000 people die from rabies around the world. Most human rabies deaths happen in Africa and Asia, where rabies remains a neglected disease. As soon as symptoms appear, human rabies is usually fatal due to acute, progressive encephalomyelitis. Although humans take precautions against rabies, sporadic outbreaks still occur in wild populations, indicating that factors that govern virus transmission and spread remain unclear. A great deal is unknown about the evolution of rabies viruses and other lyssaviruses. Because lyssaviruses are highly neurotropic, they infect the nervous system by breaking through the skin barrier. The transmission of rabies is largely dependent on domestic dogs. In addition to being part of the daily lives, domestic dogs are also part of our surroundings, which makes them more likely to contract zoonotic diseases. To eliminate rabies from domestic dog populations, which are the most dangerous vectors for humans, a sustained international commitment is important. Preventing clinical disease and death in domesticated and wild animals can be accomplished by vaccination and avoiding behaviors that may trigger exposure. Vaccines for wildlife and monoclonal antibodies are also being investigated as ongoing treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".