Disease recovery in bats affected by white-nose syndrome
Bibliographic record
Abstract
Processes associated with recovery of survivors are understudied components of wildlife infectious diseases. White-nose syndrome (WNS) in bats provides an opportunity to study recovery of disease survivors, understand implications of recovery for individual energetics, and assess the role of survivors in pathogen transmission. We documented temporal patterns of recovery from WNS in little brown bats (Myotis lucifugus) following hibernation to test the hypotheses that: 1) recovery of wing structure from WNS matches a rapid timescale (i.e., about 30 days) suggested by data from free-ranging bats; 2) torpor expression plays a role in recovery; 3) wing physiological function returns to normal alongside structural recovery; and 4) pathogen loads decline quickly during recovery. We collected naturally infected bats at the end of hibernation, brought them into captivity, and quantified recovery over 40 days by monitoring body mass, wing damage, thermoregulation, histopathology of wing biopsies, skin surface lipids, and fungal load. Most metrics returned to normal within 30 days although wing damage was still detectable at the end of the study. Torpor expression declined overall throughout the study but bats expressed relatively shallow torpor bouts, with a plateau in minimum skin temperature, during intensive healing between about days 8 and 15. Pathogen loads were nearly undetectable after the first week of the study , but some bats were still detectably infected at day 40. Our results suggest that healing bats face severe energetic imbalance during early recovery from direct costs of healing and reduced foraging efficiency. Management of WNS should not rely solely on actions during winter but should also aim to support energy balance of recovering bats during spring and summer.
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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) | 0.000 | 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".