Bibliographic record
Abstract
Echolocation calls of most bats are emitted at high intensities and subject to eavesdropping by nearby conspecifics. Bats may be especially attentive to “feeding buzz” calls, which are emitted immediately before attack on airborne insects and indicate the potential presence of prey in the nearby area. Although previous work has shown that some species are attracted to feeding buzzes, these studies did not provide a well-controlled test of eavesdropping, as comparisons were made between responses to natural and altered signals (e.g., forward versus backward broadcasts of calls). In this study, I assessed the importance of feeding buzzes by conducting playbacks of controlled echolocation stimuli. I presented free-flying Brazilian free-tailed bats, Tadarida brasiliensis (I. Geoffroy, 1824), with echolocation call sequences in which feeding buzz calls were either present or absent, as well as a silence control. I determined levels of bat activity by counting the number of echolocation calls and bat passes recorded in the presence of each stimulus, and found significantly greater bat activity in response to broadcasts that contained feeding buzzes than to broadcasts without feeding buzzes or to the silence control. These results indicate that bats are especially attentive to conspecific feeding buzz calls and that eavesdropping may allow a bat to more readily locate rich patches of insect prey.
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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 | 0.000 | 0.001 |
| 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".