Using masking metrics as a means to quantify effect and guide mitigation measures of underwater anthropogenic noise
Bibliographic record
Abstract
Abstract Anthropogenic noise in oceanic soundscapes is increasing, as is concern for its impacts on marine life. Until now, the potential effects have been considered by the comparison of sound levels to defined thresholds. Here, the influence of acoustic masking on a species is considered, quantifying the proportional reduction in range for acoustic signals as one means to characterize the impact of acoustic disturbance. The use of this metric is demonstrated by calculating the potential for masking communication calls and echolocation signals of southern resident killer whales (Orcinus orca) in the Salish Sea, British Columbia, subjected to significant commercial vessel traffic noise. The use of thresholds facilitates an empirical interpretation of changes in the sound field over space and time, whereas a masking metric determines when and where a whale's ability to send and receive acoustic information will be most obstructed. By considering the level of masking, the severity of a response might be distinguished. For example, a 0%–24% range reduction may be overcome by adaptive signaling, but this may not be possible when communication or echolocation range is reduced by 75% or more. This degree of masking was found in known foraging areas for southern residents, suggesting consequences to their success in finding and capturing food. Masking metrics will be useful to managers and policy makers to better understand acoustic disturbance of marine species and determine individual‐ to population‐level consequences of anthropogenic noise additions to soundscapes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".