Seasonal and reproductive effects on wound healing in the flight membranes of captive big brown bats
Bibliographic record
Abstract
The flight membranes of bats serve a number of physiological functions important for survival. Although flight membrane injuries are commonly observed in wild-caught bats, in most cases the damage heals completely. Previous studies examining wound healing in the flight membranes of bats have not taken into consideration energy constraints that could influence healing times. Wound healing results in increased energy demands, therefore we hypothesized that wound healing times would be slower during periods of energy conservation and/or energy output. In this study we used an 8 mm diameter circular punch tool to biopsy the wing membranes of healthy adult female big brown bats (Eptesicus fuscus) from a captive research colony to test the hypothesis that healing times will vary with seasonal temperature changes between the summer and winter seasons, and with reproductive condition between lactating and non-reproductive females. As expected, membrane biopsies took significantly longer to heal during the winter when bats were hibernating compared to the summer when bats were active. Surprisingly, no difference in healing time was observed between lactating and non-reproductive females. The wings of most bats fully healed, although some individuals showed wound expansion demonstrating that impaired healing is occasionally observed in otherwise healthy subjects.
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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 | 0.001 |
| 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) | 0.001 | 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".