Migratory stopover in the long‐distance migrant silver‐haired bat,<i>Lasionycteris noctivagans</i>
Bibliographic record
Abstract
1. Some bat species make long-distance latitudinal migrations between summer and winter grounds, but because of their elusive nature, few aspects of their biology are well understood. The need for migratory stopover sites to rest and refuel, such as used by birds, has been repeatedly suggested, but not previously tested empirically in bats. 2. We studied migrating silver-haired bats (Lasionycteris noctivagans) at Long Point, ON, Canada. We used digital radio-transmitters to track 30 bats using an array of five towers that effectively covered the entire region (c. 20 × 40 km). We measured stopover duration and departure direction, and documented movement patterns, foraging activity and roost sites. We measured body composition on arrival using quantitative magnetic resonance and simulated long-distance migration using observed body composition to predict migration range and rate. 3. Migration occurred in two waves (late August and mid-September). Most bats stayed 1-2 days, although two remained >2 weeks. One third of the bats foraged while at the site, many foraging opportunistically on nights when rain precluded continued migration. Bats roosted in a variety of tree species and manmade structures in natural and developed areas. Half of the bats departed across Lake Erie (minimum crossing distance c. 38 km) while half departed along the shoreline. 4. Simulations predicted a migration rate of c. 250-275 km per day and suggest that all but one of the bats in our study carried sufficient fuel stores to reach the putative wintering area (estimated distance 1500 km) without further refuelling. 5. Our results suggest that migrating bats stopover for sanctuary or short-term rest as opposed to extended rest and refuelling as in many songbirds. Daily torpor could reduce energy costs when not in flight, minimizing the need for extended stopovers and allowing bats to potentially complete their migration at a fraction of the time and energy cost of similar sized birds.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".