Roost selection by the solitary, foliage-roosting hoary bat (<i>Lasiurus cinereus</i>) during lactation
Bibliographic record
Abstract
Nests, roosts, and dens are an important facet of life for many animals and often provide refuge from weather and predators. Reproduction, particularly lactation, is energetically expensive. Many small mammals form maternity colonies in sheltered locations, which provides protection for offspring and mitigates the cost of staying warm. However, lasiurine bats give birth in roosts that superficially appear to offer relatively little thermal buffer. Given the consequences of a cold environment on offspring growth and the high energetic demand of thermoregulating and lactating concurrently, choosing roosts with certain microclimatic properties would be beneficial. We investigated the influence of microclimate on roost selection by lactating hoary bats ( Lasiurus cinereus (Beauvois, 1796)), a solitary foliage-roosting species. We found that roosts chosen by bats offered shelter from the wind and exposure to sunlight, and consistently had an opening that faced south. We suggest that lactating L. cinereus choose roosts based largely on a microclimate that reduces convective cooling and increases radiant heating, thereby mitigating the cost of thermoregulation and promoting rapid growth of offspring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".