Spectral probability density as a tool for marine ambient noise analysis
Bibliographic record
Abstract
The empirical probability density of the power spectral density has been successfully applied as tool to assess signal variability and sensor system performance in the seismic literature. This paper presents the application of this analysis method to underwater ambient noise measurements, and demonstrates its utility in assessing the field performance of passive acoustic monitoring systems and the statistical distribution of noise levels across the frequency spectrum. Using example datasets from an autonomous passive acoustic recorder in the Moray Firth, Scotland, UK, and a cabled subsea observatory in the Strait of Georgia, British Columbia, we show how this method can reveal data limitations such as persistent tonal components and insufficient dynamic range, and phenomena such as bimodality and outliers, which may be undetected by standard analysis techniques. We then combine this approach with conventional percentiles and spectral averages, illustrating how the underlying noise level distributions influence these metrics, and propose this technique as a standard, integrative presentation of ambient noise spectra. Finally, the paper presents cumulative probability density as a method for frequency-domain characterization of chronic noise exposure in marine acoustic habitats.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".