Bridging borders: Toward alignment of environmental regulations in the Salish Sea for whale conservation
Bibliographic record
Abstract
The Salish Sea is a dynamic and ecologically significant inland sea on the west coast of North America. This area has supported Indigenous and Tribal communities for millennia. With increasing human activity, this area is now one of the most heavily used coastlines in the world, and as such, has been experiencing declines in both biodiversity and ecosystem health. Within the Salish Sea, cetacean species, especially killer whales ( Orcinus orca ) and humpback whales ( Megaptera novaeangliae ), are under pressure from vessel disturbance, prey availability, water pollution, and habitat degradation. Numerous policies and regulations across both Canada and the United States have been enacted to protect coastal ecosystems and reduce these threats on cetaceans, however there are challenges in ensuring adequate overlap of protections between state, provincial, and federal measures. This study gathered information and insights on alignments and gaps in policies and regulations covering the Salish Sea which pertain to killer and humpback whales and some key prey species. Numerous discrepancies were highlighted, especially concerning vessel approach distances, fisheries management, critical habitat designation criteria, and pollution standards. These discrepancies are contributing to the imminent extinction risk for the endangered Southern Resident killer whale population. This study proposes that establishing aligned recovery strategies, especially for Southern Residents and salmon, leveraging new science to expand existing protections to further reduce vessel disturbance and strike risk, and increasing Indigenous and Tribal co-management of the Salish Sea ecosystem will enhance transboundary cooperation and improve the long-term outlook for whales and their prey.
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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".