Dietary protein and reproductive condition influence wound healing in the Jamaican fruit bat, <i>Artibeus jamaicensis</i>
Bibliographic record
Abstract
Dietary protein is essential for successful wound healing. When animals that feed primarily on fruits that are poor in protein and micronutrients encounter periods—during migration, reproduction, and wound healing—they require more protein than their sugar-rich diets typically provide. We hypothesize that increased consumption of insects (protein) may be especially important during metabolically intensive periods, supporting faster healing rates. We examined flight membrane wound healing in Jamaican fruit bats, Artibeus jamaicensis (Leach, 1821). We separated populations of intact and castrated males, and nonreproductive females into control and protein-enriched diet groups, then took a 3 mm diameter biopsy of the wing membrane. Wounds were photographically monitored, and wound area data were fitted to a sigmoidal function estimating the halfpoint (inflection point of sigmoid), rate of healing (slope of sigmoid). Across groups those individuals that fully healed during our 32-day observation period did so in 20.91 ± 4.81 days. We found that females reached the halfpoint faster in the protein-enriched group than the control group. Wound expansion occurred less frequently in bats fed the protein-enriched diet. Diet also impacted the shape of the wound healing relationship; we found no other significant effects of diet or male reproductive status on wound healing.
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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) | 0.001 | 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".