Singing silver‐haired bats ( <i>Lasionycteris noctivagans</i> )
Bibliographic record
Abstract
Abstract Characterizing sounds produced by animals can lead to better understanding of their behavioral ecology and conservation. While considerable focus has been on signals used by bats for echolocation, there has been less emphasis on nonecholocation sounds. We describe songs (i.e., acoustic vocalizations with distinctive syllable types in series or in complex motifs) produced by silver‐haired bat ( Lasionycteris noctivagans ). Songs, characterized by a sequence (song phrase) of 3 distinct vocalization types, were confirmed by observing free‐flying, silver‐haired bats at mine hibernacula in British Columbia, Canada. The song patterns were relatively consistent with each song phrase consisting of a lead call, followed by a droplet call, and finishing with a series of multiple chirp calls. The function of the songs is unknown, however, as other bat species produce songs for mating, we propose silver‐haired bat songs may similarly be associated with courtship or mating. Alternative functions cannot be ruled out, particularly because we recorded some songs outside of the accepted mating period. Other research has determined peak mating of silver‐haired bats occurs in fall, and spring mating has been documented. Here we additionally provide evidence of winter mating in British Columbia. The proportion of silver‐haired bat songs recorded relative to echolocation recordings varied across locations and seasons. While we recorded songs in all months of the year, more than half of the songs were produced during winter, and 93.4% (of 1,857) were produced outside of summer months. Song production in summer could be associated with other behaviors such as learning or practice, establishing or maintaining social bonds, or male‐male competition. To provide landscape and temporal context, we summarize acoustic datasets from numerous locations in western North America where recordings were made between 2005 and 2022.
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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".