Management of Noise
Bibliographic record
Abstract
Abstract Anthropogenic noise in most parts of the Earth’s oceans is increasing. If not managed appropriately, it has the potential to significantly impact marine mammals and their ability to interact with their environment. Marine mammals rely heavily on sound for critical life functions, and anthropogenic noise can interfere with sound sensing and usage, through masking, behavioral disturbance, stress, noise-induced temporary hearing loss, and, in extreme cases, injury to tissues and organs. Depending on the severity and context of a noise exposure, noise-induced impacts to individual animals could translate to population-level impacts. This chapter highlights the importance of managing anthropogenic noise, identifying limitations in existing management approaches, and briefly covering management approaches from different jurisdictions. Significant gaps in our understanding of species responsiveness to anthropogenic noise, the status of existing marine mammal populations, and characterization of underwater noise sources present challenges for the robust prediction and management of underwater noise. Based on current knowledge, a best-practice approach to the management of anthropogenic noise is presented whereby management might best achieve desired environmental outcomes. A focus on desired environmental outcomes provides a framework for robust, adaptable, and context-dependent management of underwater noise that more effectively supports species conservation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".