Particularly Sensitive Sea Areas and Transboundary Waters: An Examination of Bilateral Management of the Salish Sea
Bibliographic record
Abstract
Abstract The Salish Sea experiences substantial vessel traffic and is vulnerable to impacts from vessel-source pollution. In response to anticipated increases in vessel traffic and risk of oil spills, the non-profit organization Friends of the San Juans advocated for the United States and Canada to adopt a transboundary Particularly Sensitive Sea Area (PSSA) through the Interna-tional Maritime Organization. However, neither State ultimately supported a PSSA proposal. This article examines the unsuccessful PSSA proposal for the Salish Sea within the broader context of the PSSA mechanism to under-stand the limitations of PSSAs and provide insight into why and how PSSA designations have changed over time. Here, the Salish Sea case presents an opportunity to examine the factors that States weigh when deciding whether to propose a PSSA, and how these factors relate to potential limita-tions of the PSSA mechanism. States with a history of transnational cooper-ation, such as the United States and Canada, may be more averse to using a PSSA mechanism when more familiar and trusted systems for bilateral co-operation exist. In this way, States may rely on existing institutions, prece-dents, and agreements for transboundary collaboration between one an-other and with indigenous communities impacted by an environmental issue.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".