Techniques for distinguishing between impulsive and non-impulsive sound in the context of regulating sound exposure for marine mammals
Bibliographic record
Abstract
Regulations designed to mitigate the effects of man-made sounds on marine mammal hearing specify maximum daily sound exposure levels. The limits are lower for impulsive than non-impulsive sounds. The regulations do not indicate how to quantify impulsiveness; instead sounds are grouped by properties at the source. To address this gap, three metrics of impulsiveness (kurtosis, crest factor, and the Harris impulse factor) were compared using values from random noise and real-world ocean sounds. Kurtosis is recommended for quantifying impulsiveness. Kurtosis greater than 40 indicates a sound is fully impulsive. Only sounds above the effective quiet threshold (EQT) are considered intense enough to accumulate over time and cause hearing injury. A functional definition for EQT is proposed: the auditory frequency-weighted sound pressure level (SPL) that could accumulate to cause temporary threshold shift from non-impulsive sound as described in Southall, Finneran, Reichmuth, Nachtigall, Ketten, Bowles, Ellison, Nowacek, and Tyack [(2019). Aquat. Mamm. 45, 125-232]. It is known that impulsive sounds change to non-impulsive as these sounds propagate. This paper shows that this is not relevant for assessing hearing injury because sounds retain impulsive character when SPLs are above EQT. Sounds from vessels are normally considered non-impulsive; however, 66% of vessels analyzed were impulsive when weighted for very-high frequency mammal hearing.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".