White Nose Syndrome in Bats: Study 2: (Shelley) Inhibition of White Nose Syndrome in Hibernating Bat Colonies:Identification of Antifungal Agents
Bibliographic record
Abstract
Since it first appeared in 2006, White-Nose Syndrome (WNS) has been devastating bat populations in caves across Northeastern US. Caused by the fungus Geomyces destructans, WNS has spread into caves as far west as Oklahoma and as far north as Canada, with over 115 cave hibernacula are now affected. To reduce the spread of this pathogen and protect endangered bat species, we are looking for compounds that can be used to treat bats in situ. We have focused on testing natural organic compounds that are able to prevent the growth and further transmittance of G. destructans, without harming bats or the native cave ecology. Various compounds have been tested to prevent the growth of the closely related Geomyces pannorum using two different competition assays: 1) a disk diffusion assay; and 2) a direct application of the chemical solution. In order to specifically target Geomyces species, the same tests were also performed using the unrelated fungi Penicillium pinophiolium and Aspergillus brasiliensis. Our results suggest that a number of compounds have the potential to prevent the spread of WNS and are currently being tested on the pathogen G. destructans and on healthy bat populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".