RABIES PREVALENCE IN MIGRATORY TREE-BATS IN ALBERTA AND THE INFLUENCE OF ROOSTING ECOLOGY AND SAMPLING METHOD ON REPORTED PREVALENCE OF RABIES IN BATS
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) The migratory tree-roosting hoary bat (Lasiurus cinereus) and silver-haired bat (Lasionycteris noctivagans) are among the bat species with the highest reported prevalence of rabies in North America. However, bats submitted for rabies testing typically have been those that have come in contact with humans or pets. Given the roosting ecology of L. cinereus and L. noctivagans, contact with healthy individuals of these species is expected to be rare, with a bias in contact and submission of infected individuals and thus an overestimation of rabies prevalence. We tested 121 L. cinereus and 96 L. noctivagans specimens, collected during mortality surveys at wind energy facilities in Southern Alberta, Canada in 2007 and 2008, for rabies. None of the L. cinereus (0%) and one L. noctivagans (1%) tested positive for rabies. Prevalence of rabies was significantly lower than previously reported estimates, passive and active, for L. cinereus and L. noctivagans. In a review of the literature including multiple bat species, we found a significant difference in estimates of rabies prevalence based on passive versus active surveillance testing. Furthermore, roosting ecology influenced estimates of rabies prevalence, with significantly higher prevalence among passive surveillance submissions of nonsynanthropic species compared to synanthropic species, a trend not evident in active surveillance reports. We conclude that rabies prevalence in randomly collected L. cinereus and L. noctivagans is low and comparable to active surveillance estimates from other species (#1%), and that roosting ecology influences estimates of rabies prevalence among bats submitted to public health laboratories in North America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".