High duty cycle echolocation and prey detection by bats
Bibliographic record
Abstract
There are two very different approaches to laryngeal echolocation in bats. Although most bats separate pulse and echo in time by signalling at low duty cycles (LDCs), almost 20% of species produce calls at high duty cycles (HDCs) and separate pulse and echo in frequency. HDC echolocators are sensitive to Doppler shifts. HDC echolocation is well suited to detecting fluttering targets such as flying insects against a cluttered background. We used two complementary experiments to evaluate the relative effectiveness of LDC and HDC echolocation for detecting fluttering prey. We measured echoes from fluttering targets by broadcasting artificial bat calls, and found that echo amplitude was greatest for sounds similar to those used in HDC echolocation. We also collected field recordings of syntopic LDC and HDC bats approaching an insect-like fluttering target and found that HDC bats approached the target more often (18.6% of passes) than LDC bats (1.2% of passes). Our results suggest that some echolocation call characteristics, particularly duty cycle and pulse duration, translate into improved ability to detect fluttering targets in clutter, and that HDC echolocation confers a superior ability to detect fluttering prey in the forest understory compared with LDC echolocation. The prevalence of moths in the diets of HDC bats, which is often used as support for the allotonic frequency hypothesis, can therefore be partly explained by the better flutter detection ability of HDC bats.
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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 | 0.000 |
| 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 | 0.000 | 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".