Bat bites and rabies: the Canadian scene
Bibliographic record
Abstract
Bats are susceptible to rabies. Although bats may appear to be asymptomatic carriers of rabies for a few days, eventually they fall ill to the viral infection and die. Two of at least four bat-specific variants of rabies virus in Canada have killed humans. Rabies is usually transmitted by biting, but bats are small mammals so their bites may go unnoticed. People exposed to rabid animals should receive postexposure prophylaxis (PEP). With 60 known human deaths from 1950 to 2009, rabies is rare in Canada and the United States of America compared with India where it kills over 100 people annually. In Asia and Africa, most human rabies is acquired from dog bites. In Brazil, dog and bat bites together account for >80% of human rabies. In Canada, rabies is a disease primarily confined to wildlife (foxes, racoons, skunks, and bats). The public image of bats is negatively affected by their association with diseases. Too often bats are victimized by allegations of their role in deadly diseases such as rabies, Ebola, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS). In general, bats are not dangerous, but humans should seek treatment if they are bitten by one. (Graphical abstract shows a 4-g elegant myotis biting MBF’s finger—photo by Sherri and Brock Fenton.)
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 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".