Sound exposure level as a metric for analyzing and managing underwater soundscapes
Bibliographic record
Abstract
The auditory frequency weighted daily sound exposure level (SEL) is used in many jurisdictions to assess possible injury to the hearing of marine life. Therefore, using daily SEL to describe soundscapes would provide baseline information about the environment using the same tools used to measure injury. Here, the daily SEL from 12 recordings with durations of 18-97 days are analyzed to: (1) identify natural soundscapes versus environments affected by human activity, (2) demonstrate how SEL accumulates from different types of sources, (3) show the effects of recorder duty cycling on daily SEL, (4) make recommendations on collecting data for daily SEL analysis, and (5) discuss the use of the daily SEL as an indicator of cumulative effects. The autocorrelation of the one-minute sound exposure is used to help identify soundscapes not affected by human activity. Human sound sources reduce the autocorrelation and add low-frequency energy to the soundscapes. To measure the daily SEL for all marine mammal auditory frequency weighting groups, data should be sampled at 64 kHz or higher, for at least 1 min out of every 30 min. The daily autocorrelation of the one-minute SEL provides a confidence interval for the daily SEL computed with duty-cycled data.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".