Trade-offs and synergies in the management of environmental pressures: a case study on ship noise mitigation
Bibliographic record
Abstract
Underwater noise from shipping is increasingly recognized as a significant pollutant that can have a range of detrimental effects on marine organisms. However, ships impact marine life in more than one way. From a management perspective, a holistic approach could provide a more successful way to minimize the impact of ship traffic than sequential, single-pressure mitigation. In this paper, we assess how other shipping pressures are affected by six noise mitigation measures: ship speed restriction, rerouting, convoying, frequent hull/propeller cleaning, ship-quieting technologies, and incentivising fewer, larger ships. Here, we present and apply a framework to evaluate the synergies and trade-offs in the implementation of mitigation measures to better consider cumulative effects and advance effective, and holistic management. Using expert judgement and peer-reviewed literature, we evaluate each of the proposed mitigation measures to determine whether they are likely to have synergistic or trade-off effects on the impacts from other shipping pressures, the scale of the effect, and the strength of the evidence. Overall, speed reduction has mostly synergies with only weak trade-offs in the other shipping pressures. Frequent hull and propeller cleaning has fewer synergies, but also very few trade-offs, whereas convoying is expected to be the measure with the most trade-offs with other pressures. Re-routing and the incentivization of fewer larger ships have mostly unclear outcomes, because this will depend on the circumstances of implementation. We conclude that carefully considered and thoughtfully implemented mitigation measures can lead to multiple benefits across shipping pressures.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".