BENEFITS OF LIVING IN A BUILDING: BIG BROWN BATS (EPTESICUS FUSCUS) IN ROCKS VERSUS BUILDINGS
Bibliographic record
Abstract
Individuals of some species of bats roost in human-made structures despite the apparent availability of natural roosts. We compared patterns of thermoregulation in relation to microclimate and compared reproductive timing for maternity colonies of big brown bats (Eptesicus fuscus) roosting in natural and building roosts in the prairies of southeastern Alberta. During pregnancy, bats roosting in buildings used torpor less frequently than did rock-roosting bats, but achieved lower body temperatures when torpid. Less-frequent use of torpor leaves more active days for fetal development, and bats in building roosts gave birth earlier than those in rock roosts. We observed predators and predation in rock roosts, but not in building roosts, and suggest that bats roosting in rocks use shallower torpor to remain vigilant. Patterns of torpor use suggest that bats in buildings save more energy than rock-roosting individuals by roosting in the warmer microenvironments of buildings and by achieving lower body temperatures when ambient conditions are cold and foraging is not productive. The warmer building roosts are also conducive to juvenile growth, and young building-roosting bats fledged 1–2 weeks before rock bats. We propose that advantages for bats roosting in buildings (lower predation risk, earlier births, faster juvenile growth rates, and increased energy savings) lead to greater long-term reproductive success for building-roosting bats and make buildings preferred roosts.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".