The trade-off analysis for the mitigation of underwater noise pollution from commercial vessels: Case study – Trans Mountain project, Port of Vancouver, Canada
Bibliographic record
Abstract
Although shipping has significant positive effect on human civilization, it introduced negative environmental impacts such as oil, air, and plastic pollutions. Many negative externalities through international and local regulations have been in place, and preventive actions have been taken to monitor and control. However, underwater noise pollution as an emerging negative shipping impact has not been well introduced to society nor appropriately regulated in international scale. Because of traffic density and the presence of sensitive marine species in some parts of the world, the negative social and environmental impacts of underwater noise pollution become more critical. Haro Strait due to high shipping traffic and presence of vulnerable marine species such as Southern Resident Killer Whale is a good example. The majority of ocean-going vessels transiting to Vancouver and vice versa pass through the corridor which includes Haro Strait. Tankers currently represent about 2% of total ship traffic visiting the Port of Vancouver; however, regarding the Trans Mountain Pipeline Expansion Project, the traffic density will grow by 11%, which will enhance the adverse impacts of underwater noise pollution on marine mammals. This study, by considering the features and characteristics of the area and the project, proposed four scenarios and modelling. The article by developing simulations and utilizing the Multiple Criteria Decision Making (Multiple Attribute Decision Making) algorithms and Technique for Order of Preference by Similarity to Ideal Solution techniques strives to trade-off between the environmental (noise and CO 2 emission) and economical (fuel cost) aspects of the project to enhance the Decision Support System to promote sustainable development. This will help the decision makers to have a multi-dimensional thinking instead of the single-dimensional thinking in addressing and tackling the negative externalities of the Trans Mountain project in the area. Moreover, at the end of each scenario, a sensitivity analysis will be conducted to provide a clean environment for decision makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".