Horseshoe bats use not changes in echo delay but Doppler shift to perceive approaching objects
Bibliographic record
Abstract
Echolocating bats use echo delay for target ranging and Doppler shift information for relative velocity recognition. However, how they perceive moving objects remains unclear. To investigate this question, we played back echolocation pulses in real-time as virtual echoes to Japanese horseshoe bats (Rhinolophus ferrumequinum nippon) on a perch in a flight room. Since echoes coming back from an approaching object are theoretically characterized by both changes in echo delay and the presence of Doppler shift, we reproduced an artificial approaching object by encoding these two acoustic parameters in the virtual echoes. As a result, only Doppler shift evoked bats flight reaction, showing that they use only Doppler shift and not change in echo delay to perceive approaching objects. Also, we played back only constant frequency (CF) component and confirmed that they use the CF component to detect Doppler shift. Furthermore, as a response to the Doppler shift in the perceived echo, bats increased the bandwidth of the terminal component of their pulse. Surprisingly, this response occurred in the very first pulse after Doppler shift, which indicates bats can adapt their echolocation pulse characteristics to changing situations within a pulse. [This work was supported by JSPS KAKENHI Grant Nos. 18H03786 and 16H06542.]
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".