Seasonal and Nightly Activity Patterns of Migrating Silver-Haired Bats (Lasionycteris noctivagans) Compared to Non-Migrating Big Brown Bats (Eptesicus fuscus) at a Fall Migration Stopover Site
Bibliographic record
Abstract
Migrating temperate bats travel hundreds and perhaps thousands of kilometers, which necessitates making use of stopover sites. Migratory birds use stopover sites to rest and refuel for subsequent migratory flights, but it isn't clear what bats do during their comparatively brief stopovers. We used acoustic monitoring to compare activity patterns of migrating silver-haired bats (Lasionycteris noctivagans) and resident big brown bats (Eptesicus fuscus) at Long Point, Ontario, Canada. From September 1 to October 31 2020 we recorded 4,333 echolocation passes from our two focal species and quatified feeding buzzes in those recordings. Migratory L. noctivagans passed through in two waves, one of which had been identified by previous study, and a second suggesting that the migratory period may be longer than previously identified. Eptesicus fuscus was primarily active only in the early and late parts of the night, in contrast to the activity pattern of L. noctivagans which were similarly active and foraging at dusk and dawn, but also during the middle parts of the night. Our acoustic monitoring data complement previous data collected from bat captures and radiotelemetry to provide further insight into stopover behaviors and ecology of temperate migratory bats.
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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".