Variability of the inter-pulse interval in sperm whale clicks with implications for size estimation and individual identification
Bibliographic record
Abstract
Sperm whales generate multi-pulsed clicks for echolocation and communication with an inter-pulse interval (IPI) determined by the size of their hypertrophied sound producing nose. The IPI has therefore been used to estimate body size and distinguish between individuals, and it has been hypothesized that conspecifics may use IPIs to recognize each other. However, the degree to which IPIs vary within individuals has not explicitly been tested, and therefore the inherent precision of this measure and its applicability for size estimation for researchers and sperm whales alike remain unknown. Here, the variability in IPI from both animal-borne Dtags and far-field recordings from echolocating and communicating sperm whales is quantified. Three different automatic methods (envelope, cepstrum, and cross-correlation) are tested and it is found that the envelope approach results in the least dispersion. Furthermore, it is shown that neither growth, depth, nor recording aspect fully explains the observed variability among clicks recorded from the same individual. It is proposed that dynamics in the soft structures of the nose are affecting IPIs, resulting in a variation of approximately 0.2 ms. Therefore, it is recommended that this variation be considered in IPI studies and that IPIs may have limited functionality as an identity cue among large groups of conspecifics.
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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 | 0.001 | 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) | 0.001 | 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".