Assessing cumulative impacts of underwater noise with other stressors on marine mammals.
Bibliographic record
Abstract
Cumulative impact assessments (CIAs) are an often unmet requirement in many environmental impact assessment processes. However, marine mammals are typically exposed to multiple human activities and pollutants including noise, which can combine in various ways including through chronic stress responses. To address the issue, the Okeanos Foundation held an international, multi-disciplinary workshop in Monterey, CA (August 2009). Participants considered three aspects: how currently available tools for regionally mapping several anthropogenic pressures on the environment could be applied to species management, how the reported consequences in marine mammals of exposure to these pressures and their known interactions within an individual could be modeled, and how population modeling could include cumulative impacts. Participants felt that all three approaches could be realized in certain data-rich marine mammal populations, which could then be used as examples for informing management decisions in other marine mammals. The population modeling for cumulative impacts on Western gray whales and Southern and North Atlantic right whales is currently underway. Participants believed that marine spatial planning would facilitate better CIAs and that reducing ocean noise is an achievable goal that will help marine life cope with less tractable threats such as climate change.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".