Automatic acoustic estimation of sperm whale size distributions achieved through machine recognition of on-axis clicks
Bibliographic record
Abstract
The waveforms of individual sperm whale clicks often appear as multiple pulses, which are the product of a single pulse reverberating throughout the spermaceti organ. Since there is a relationship between spermaceti organ size and total body size, it is possible to estimate a whale's length by measuring the inter-pulse intervals (IPIs) within its clicks. However, if a click is recorded off-axis, the IPI corresponding to spermaceti organ length is usually obscured. This paper presents an algorithm for automatically estimating the "true" IPIs of sperm whales in a recording by measuring them from on-axis clicks only. The routine works by classifying detected clicks with a support vector machine, assessing the stability of their IPIs, and then clustering the stable IPIs using Gaussian mixture models. Results show that the routine is very accurate in obtaining reliable IPIs, but has a high false negative rate. Nonetheless, since sperm whales click very frequently, it is possible to obtain useful IPI distributions with only a few minutes of recording. This algorithm makes it possible to estimate the body lengths of multiple sperm whales automatically with only one hydrophone. An implementation is available for download at http://whitelab.biology.dal.ca/CABLE/cable.htm.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
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".