Data from: Modelling the potential efficacy of treatments for white-nose syndrome in bats
Bibliographic record
Abstract
1. The fungal disease white-nose syndrome (WNS) has caused mass mortality in some species of North American bats during hibernation. 2. We use population viability models to test if a hypothetical WNS treatment or management action could facilitate the recovery of WNS-affected little brown myotis (Myotis lucifugus) populations. We modelled scenarios altering three parameters: (1) WNS severity (population growth rate of WNS-affected populations; λWNS); (2) proportion of population treated; and (3) treatment improvement in winter survival (TIWS). 3. Our models predict that a treatment or management action that targets an entire population with a TIWS of 40% (the average TIWS in bat trials to date) will cause a population to stabilize or increase if WNS causes an annual decline of less than 70% (i.e. λWNS>=0.30). However, for severe WNS (λWNS=0.10), the TIWS must be at least 54% to cause the population to stabilize or increase. Where only a proportion of a WNS-affected population is treated, population stability is much harder to achieve unless the impact of WNS attenuates over time. 4. Our models suggest that a treatment or management action only facilitates the recovery of WNS-affected populations if WNS is mild, a large proportion of bats can be treated, TIWS is high, and/or WNS severity attenuates over time. 5. Synthesis and applications. We modelled the predicted abundance trajectory of white-nose syndrome (WNS)-affected little brown myotis (Myotis lucifugus) populations in response to hypothetical treatment or management actions. Our two types of models incorporate the complete range of possible scenarios varying three parameters: (1) population growth rate of the WNS-affected population, (2) the improvement in winter survival associated with the treatment or management action, and (3) the proportion of the population treated. We suggest that our models, which can be explored using online Shiny applications, should be used in the planning phase of treatment or management action programs for WNS.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".