Acoustic shadows help gleaning bats find prey, but may be defeated by prey acoustic camouflage on rough surfaces
Bibliographic record
Abstract
Perceptual abilities of animals, like echolocating bats, are difficult to study because they challenge our understanding of non-visual senses. We used novel acoustic tomography to convert echoes into visual representations and compare these cues to traditional echo measurements. We provide a new hypothesis for the echo-acoustic basis of prey detection on surfaces. We propose that bats perceive a change in depth profile and an 'acoustic shadow' cast by prey. The shadow is more salient than prey echoes and particularly strong on smooth surfaces. This may explain why bats look for prey on flat surfaces like leaves using scanning behaviour. We propose that rather than forming search images for prey, whose characteristics are unpredictable, predators may look for disruptions to the resting surface (acoustic shadows). The fact that the acoustic shadow is much fainter on rougher resting surfaces provides the first empirical evidence for 'acoustic camouflage' as an anti-predator defence mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".