Potential Spread of White-nose Syndrome of Bats to the Northwest: Epidemiological Considerations
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) In the past several years, a fungal epidemic has devastated hibernating bat populations in eastern North America, with an estimated loss of 5.7 to 6.7 million bats as of January 2012. The potential for the disease to spread to bat populations in the western states and Canadian provinces remains unknown, but is cause for significant concern. This wildlife health crisis has been dubbed white-nose syndrome (WNS), for the distinctive white fungal growth that appears on the muzzles, ears, and wing membranes of affected bats. This fungus, the recently named species Geomyces destructans, has been determined to be the causal agent of WNS. However, relatively little is currently known about the ecology of this organism, its potential for invasiveness in the Northwest, and about how disease spreads within and between bat populations. Our purpose here is to summarize current epidemiological knowledge about WNS, in an ecological context relevant to efforts to understand the epidemic and predict its potential to spread to western bat populations. Because of strong similarities between WNS and some invasive fungal diseases of crops and forests, our approach is to incorporate epidemiological perspectives borrowed from the field of plant pathology as well as from wildlife pathology. We highlight research needs that will help to understand, predict, and manage this devastating wildlife disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".