Winter Ecology and Ecophysiology of Prairie-Living Big Brown Bats (Eptesicus Fuscus)
Bibliographic record
Abstract
Hibernation allows animals to survive lengthy periods of energetic deficit, but is not without costs. Hypometabolism, low body-temperature, and inactivity are associated with a variety of costs such as immuno-incompetence, dehydration, and build up of harmful metabolites. Additionally, conditions within hibernacula have a profound influence on hibernation patterns and survival. Periodic arousals and site selection are thought to mitigate these costs, and often involve timing arousals to foraging opportunities and overwintering in locations with stable temperatures and high humidity. I studied prairie-living big brown bats (Eptesicus fuscus) that overwinter in rock crevices and take flight outside of the hibernacula despite a lack of foraging opportunity. My goal was to describe their winter ecology and behaviour, and investigate reasons for winter flight. I found that E. fuscus in my study area use relatively dry hibernacula compared to known cavernous sites and show fidelity to sites between and within years. I found that temperature and wind are important predictors of winter flight, and that arousals remain under diurnal influence. My data suggest that individuals from this particular population spend the majority of their winter energy-stores during steady-state torpor and have mechanisms to decrease evaporative water loss during hibernation. I found typical levels of dehydration as winter progressed and my data indicate no use by bats of a supplemental water source. My research elucidates novel behaviours and traits of this population of E. fuscus, and reduces the paucity of knowledge about winter bat-ecology in the prairies.
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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) | 0.002 | 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".