Evolution of high duty cycle echolocation in bats
Why this work is in the frame
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Bibliographic record
Abstract
Duty cycle describes the relative 'on time' of a periodic signal. In bats, we argue that high duty cycle (HDC) echolocation was selected for and evolved from low duty cycle (LDC) echolocation because increasing call duty cycle enhanced the ability of echolocating bats to detect, lock onto and track fluttering insects. Most echolocators (most bats and all birds and odontocete cetaceans) use LDC echolocation, separating pulse and echo in time to avoid forward masking. They emit short duration, broadband, downward frequency modulated (FM) signals separated by relatively long periods of silence. In contrast, bats using HDC echolocation emit long duration, narrowband calls dominated by a single constant frequency (CF) separated by relatively short periods of silence. HDC bats separate pulse and echo in frequency by exploiting information contained in Doppler-shifted echoes arising from their movements relative to background objects and their prey. HDC echolocators are particularly sensitive to amplitude and frequency glints generated by the wings of fluttering insects. We hypothesize that narrowband/CF calls produced at high duty cycle, and combined with neurobiological specializations for processing Doppler-shifted echoes, were essential to the evolution of HDC echolocation because they allowed bats to detect, lock onto and track fluttering targets. This advantage was especially important in habitats with dense vegetation that produce overlapping, time-smeared echoes (i.e. background acoustic clutter). We make four specific, testable predictions arising from this hypothesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it