COULD WHITE-NOSE SYNDROME MANIFEST DIFFERENTLY IN MYOTIS LUCIFUGUS IN WESTERN VERSUS EASTERN REGIONS OF NORTH AMERICA? A REVIEW OF FACTORS
Bibliographic record
Abstract
White-nose syndrome (WNS) has notably affected the abundance of Myotis lucifugus (little brown myotis) in North America. Thus far, substantial mortality has been restricted to the eastern part of the continent where the cause of WNS, the invasive fungus Pseudogymnoascus destructans, has infected bats since 2006. To date, the state of Washington is the only area in the Western US or Canada (the Rocky Mountains and further west in North America) with confirmed cases of WNS in bats, and there the disease has spread more slowly than it did in Eastern North America. Here, we review differences between M. lucifugus in western and eastern parts of the continent that may affect transmission, spread, and severity of WNS in the West and highlight important gaps in knowledge. We explore the hypothesis that western M. lucifugus may respond differently to WNS on the basis of different hibernation strategies, habitat use, and greater genetic structure. To document the effect of WNS on M. lucifugus in the West most effectively, we recommend focusing on maternity roosts for strategic disease surveillance and monitoring abundance. We further recommend continuing the challenging work of identifying hibernation and swarming sites to better understand the microclimates, microbial communities, and role in disease transmission of these sites, as well as the ecology and hibernation physiology of bats in noncavernous hibernacula.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".