Data from: Modelling the potential efficacy of treatments for white-nose syndrome in bats
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it