Ultrasonic and superfast: Design constraints on echolocation in bats
Bibliographic record
Abstract
Recent work from our group demonstrates that two exceptional characteristics of bat biosonar—bats’ extremely high call emission rates and these calls’ ultrasonic frequencies—reflect biomechanical constraints of the vocal apparatus. We hypothesized that smaller bats, with their smaller mouths, emit higher frequencies to achieve sufficiently directional sonar beams, and that variable directionality is critical for bats. We found that six aerial hawking, vespertilionid bat species produced sonar beams of similar shape and volume, and we predict that many bats adjust their acoustic field of view to suit habitat and task. We speculate that sonar beam shape has been an evolutionary constraint on echolocation and explains the bat size-call frequency correlation. During the terminal phase of an aerial hawking attack on an insect, bats produce a “buzz,” increasing information update rates by producing >160 calls/second. We discovered that bats use specialized superfast muscles to power these rapid call rates. We also show that laryngeal motor performance, not call-echo overlap, limits maximum call rate. We suggest that the advantages of rapid auditory updates on prey movement have selected for superfast laryngeal muscle. Taken together, our results provide further evidence that bat biosonar is a dynamic sensory system, a sensory system that allows bats to adjust and optimize their acoustic fields of view and to update their auditory scene at rates >160 times/second to optimize airborne prey detection and tracking.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".