Changes in underground roosting patterns to optimize energy conservation in hibernating bats
Bibliographic record
Abstract
Non-migratory bats in colder climates use hibernation to survive winter. By reducing metabolic rate (i.e., using torpor), bats can survive winter on stored fat reserves. During hibernation, bats arouse from torpor and may move within the hibernaculum, a process called “internal migration”. We hypothesized that internal migration occurs to optimize hibernation energetics in that bats move to select a microclimate to minimize energy expenditure both by seeking cooler areas of the hibernacula and avoiding those with large temperature fluctuations. Early in the winter, we observed that 62% of bats were roosting in the warmer, less energy efficient, deepest 50% of an abandoned mine hibernaculum. Late in the season, there was a shift towards the cooler entrance area, thereby decreasing energy demands during the torpid period, with 78% of bats in the mine roosting within 50 m of the entrance. Although there was no significant effect of hibernation period (i.e., early vs. late winter) on the number of bats in huddles, the largest huddles occurred close to the entrance in late winter. To fully understand and manage bat populations, it is important to understand that hibernation is a dynamic process with bats moving and interacting with one another throughout the season.
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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".