U.S. Geological Survey science strategy to address white-nose syndrome and bat health in 2025–2029
Bibliographic record
Abstract
Since its discovery in 2006, the fungal disease known as white-nose syndrome (WNS) has killed millions of bats.Of the 47 bat species native to the conterminous United States, Alaska, Hawaii, and Canada, 12 have been affected by WNS, including 3 endangered species and 1 proposed endangered species.WNS has also been detected in 40 States and 9 Canadian Provinces.U.S. Geological Survey (USGS) scientists have been critical in identifying the causal fungus for WNS (Pseudogymnoascus destructans [Pd]), characterizing the effects of WNS, and tracking the spread of Pd in many bat populations in North America.The mission of the USGS WNS and Bat Health Science Team is to deliver integrated science in order to build resiliency into free-ranging bat populations through more effective WNS management, build capacity for bat health science, and enhance bat health information sharing across USGS science centers and cooperative research units as well as with stakeholders.The USGS can play an important role in supporting regional and national capacity building by providing resources and guidance to local, State, and Tribal management entities and by providing tools to enhance disease management.The USGS Ecosystems Mission Area's Biological Threats and Invasive Species Research Program is the lead Federal program for free-ranging wildlife disease research and surveillance.As of 2024, guided by the science priorities set by the WNS Steering Committee, USGS scientists are engaged in a nationwide response to WNS.This work is done in close coordination with our partners at the U.S. Fish and Wildlife Service, National Park Service, Bureau of Land Management, U.S. Forest Service of the U.S. Department of Agriculture, U.S. Department of Defense, as well as State and Tribal agencies.In addition to conducting WNS research, the USGS is mapping the spread of WNS and coordinating the North American Bat Monitoring Program (NABat) to understand how WNS and other stressors affect the status and trends of native bats across their range.The USGS is supporting the national WNS response through four science goals: (1) provide situational awareness on the health of bat populations;(2) conduct ecological studies of bats along the gradient of disease vulnerability; (3) contribute actionable science to enhance the resiliency of bat populations; and (4) implement an adaptive, holistic approach to bat health.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".