Testing the Role of Frequency-Modulated Bouts in Eptesicus fuscus
Bibliographic record
Abstract
Social vocalizations serve multiple purposes in animal communication and are particularly common in bats (Order Chiroptera). The big brown bat (Eptesicus fuscus; Family Vespertilionidae) emits downward frequency modulated (FM) sweeps that are used for echolocation. Male E. fuscus have been reported to emit a cluster of 3-4 downward FM signals that are longer in duration than echolocation calls, known as frequency-modulated bout (FMB) signals, and have been hypothesized to play a role in food competition. Other Vespertilionid species emit social calls that share spectro-temporal properties with the FMB. These social calls have a role in food competition and incite positive phonotaxis in females, so it is conceivable that the FMB also has a function in mate attraction. My thesis focused on the role of FMB social calls and if they are attractive to females. I tested female E. fuscus in acoustic playback trials presenting bats with competing signals in a two-alternative forced choice paradigm. Bats were presented with combinations of three types of synthetically-generated sounds—a natural-like FMB stimulus, a time-reversed FMB stimulus, and acoustic system noise generated by a silent signal—and I measured the relative affinity or aversion of bats towards different combinations of these signals. Bats were tested first in the fall mating season and then retested in the spring after winter hibernation season. If FMBs are mainly emitted by males for mate attraction, then I predicted females would show an affinity for them, particularly during mating season. My results somewhat corroborate the hypothesis as the females showed some affinity for these calls. These results provide a foundation for further research into understanding big brown bat mate choice and social behaviour.
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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 | 0.001 | 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".