Pearls Fungal Disease and the Developing Story of Bat White- nose Syndrome
Bibliographic record
Abstract
Two recently emerged cutaneous fungal diseases of wildlife, bat white-nose syndrome (WNS) [1] and amphibian chytridiomycosis [2], have devastated affected populations. Fungal diseases are gaining recognition as significant causes of morbidity and mortality to plants, animals, and humans [3], yet fewer than 10 % of fungal species are known [4]. Furthermore, limited antifungal therapeutic drugs are available, antifungal therapeutics often have associated toxicity, and there are no approved antifungal vaccines. The unexpected emergence of WNS, the rapidity with which it has spread, and its unprecedented severity demonstrate both the impacts of novel fungal disease upon naı̈ve host populations and challenges to effective management of such diseases. When Was WNS Discovered and What Is the Cause? The first evidence suggestive of WNS in North America is a photograph of hibernating bats with an unusual white substance on their muzzles taken in February 2006, at a cave in east central New York. Since then, WNS has spread to 19 US states and four Canadian provinces (see
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".