Chronic ocean noise and cetacean population models
Bibliographic record
Abstract
Recent years have seen rapid development of tools and approaches to model population consequences of disturbance in several marine mammal populations from high-amplitude, acute sound sources. Ocean noise from shipping and other maritime activities is now recognized as a chronic, habitat-level stressor. In order to understand population consequences of chronic ocean noise to whales and their populations, advances are needed in several key areas, which are explored in this review. One tractable way to predict population-level consequences of noise-mediated disruption of feeding, which can include both behavioural responses and foraging opportunities lost due to acoustic masking. Masking may be defined as both the process and the amount by which the threshold of hearing of one sound is raised by the presence of another. Parameterising any such model requires information on sensitivity and vulnerability of large whales to ocean noise, in which sensitivity is the degree to which marine features respond to a stressor (e.g., behavioural responses to noise or proportional reduction in foraging efficiency due to masking), and vulnerability is the probability that whales are exposed noise to which they are sensitive. Efforts are underway to provide much-needed information on hearing sensitivity in baleen whales, the role of acoustic cues in foraging, and deriving links between long-term variability in prey availability and whale demography. As this information becomes available, we expect rapid advancement on modelling population consequences of acoustic masking in baleen whales, because those efforts can leverage substantial investments in statistical methodological approaches to model population consequences of disturbance. Pathways of effects other than via foraging disruption (e.g., stress hormones affecting reproduction or disease) are possible, but we illustrate potential ways to proceed based on this tractable approach, namely noise-mediated impacts on foraging. This report highlights case studies of local, national, international, and inter-governmental efforts to monitor and reduce the contribution of global shipping to ocean ambient noise. The following outlines approaches that can be used to assess the risk to baleen whale recovery of existing levels of ocean noise, and consequently, predict the benefits likely to arise from reducing chronic ocean noise.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".