Exploring methods to maintain and improve captive big brown bat (Eptesicus fuscus) flight
Bibliographic record
Abstract
Wild-caught big brown bats (Eptesicus fuscus) experience increased mass, diminished ability to sustain flight, and increased take-off latency after three months in captivity, making them ineligible for flight experiments. These animals had been housed in small cages with ad libitum food. I quantified these changes by weighing and flying newly-caught E. fuscus, housing them under the mentioned conditions, and re-recording them after 11 weeks in captivity. I used LMMs to analyze data. Surprisingly, take-off latency decreased somewhat, though the existence of a true effect was inconclusive. Conversely, the bats had increased mass and decreased flight duration, as expected. The second part of this study examined methods, namely food availability and crawling exercise, to improve E. fuscus flight. I housed some subjects in a large colony space that allowed free flight, and I did not explicitly exercise them. I housed other subjects in small cages that restrict exercise, and exercised some through crawling, which I hypothesized would improve flight. Finally, I restricted some subjects’ food intake and gave ad libitum access to others. I hypothesized the restricted diet would improve flight, while ad libitum access would diminish it. I recorded flights three times per night, twice a week for 7.5 weeks. I analyzed the data using linear mixed-effects models. Restricting food intake had a positive effect on flight duration; ad libitum food access did not have a conclusive effect. Also, the crawling exercise did not positively affect flight; bats housed in the colony had increased flight duration. Apparently, having space for voluntary flight and being explicitly flown twice per week can improve flight. Take-off latency did not have conclusive results, though surprisingly, the bats on restricted diets had somewhat increased take-off latencies. In conclusion, restricting diet or explicitly flying bats housed in the colony are two methods that can be employed to improve flight duration to allow for scientific studies requiring flight.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".