Particularly Sensitive Sea Area: An International Tool to Reduce the Risk of Accidents Associated with Vessel Traffic in the Salish Sea
Bibliographic record
Abstract
Learn results from the Particularly Sensitive Sea Area feasibility study that analyzed how this international regulation can protect important ecological and cultural values in the Salish Sea impacted at risk by increased vessel traffic. The Particularly Sensitive Sea Area (PSSA) Feasibility Study for the Salish Sea investigated the possible benefits and costs of enhancing the management regime of the areas surrounding the shipping lanes surrounding the San Juan Island National Monument, and British Columbia’s Marine Provincial Parks through the creation of a Particularly Sensitive Sea Area. This potential designation can only be achieved if the area is particularly sensitive (in ecological and cultural terms). While such PSSA designations have been recognized in other parts of the United States and the international community, they have not been considered in the context of the highly vulnerable Salish Sea. Learn what needs to be achieved, how this was done elsewhere, with tribes, citizens, businesses, eco-tourism operators, an decision-makers, and what the implications are for the Salish Sea. Discuss the next steps needed to move the PSSA forward for Canada and the United States. This PSSA Feasibility study was conducted by the FRIENDS of the San Juans with support from the Samish Indian Nation. Presenter Bio. Dr. Alexander Gillespie is the Pro-Vice Chancellor of Research at the University of Waikato, New Zealand and a professor of international law and the laws of war. He is the author of 11 books and over 50 peer-reviewed academic articles. He is commonly recognized as one of the global academic leader in the area of conservation, biodiversity and international environmental law. Professor Gillespie has worked extensively on diplomatic matters for the Ministry of Foreign Affairs, and the Department of Conservation in New Zealand. He has lead foreign delegations for New Zealand, as well as working for international organizations such as UNESCO where he was elected as the Rapporteur for the World Heritage Convention. In addition, Professor Gillespie has provided commissioned work for the United Nations, the Commonwealth Secretariat, national governments (in addition to New Zealand) and numerous corporate and non-profit organizations in Europe, North America, and Australasia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".