Urban seas as hotspots of stress in the Anthropocene ocean: The Salish Sea example
Bibliographic record
Abstract
Coastal seas and estuaries are among the most productive ecosystems on Earth and have long attracted human activity. Yet, urbanization pressures are intense and are compounded by accelerating climate stresses. Urban seas are now hotspots of stress in the Anthropocene ocean. The Salish Sea stands out as one of a few highly functioning urban seas in the world, boasting ecological riches and thriving coastal communities and industries, including tourism. For over 10,000 years the region has supported Indigenous peoples; now it is home to a growing population of almost nine million people, concentrated in and near the major cities of Seattle, Washington, and Vancouver, British Columbia. Increasing urbanization combined with intensifying climate stress is degrading the Salish Sea and acutely affecting communities already experiencing marginalization. Current environmental impacts include acidifying waters, hypoxia, and intense heat waves, all of which have had measurable impacts within the ecosystem. A recent synthesis of this system identified key domains for solutions, which we generalize here for invoking positive change in global urban seas: 1) innovation in data collection, curation, and integration using a systems approach in science and management; 2) sharing place-based knowledge to sustain community-based action; and 3) aligning science and policy with ecosystem boundaries. The differing governance and socio-political settings across two countries and numerous Indigenous nations creates a complex challenge in ecosystem management. Developing actionable solutions for people and the biota of the Salish Sea can create a global example of a sustainably managed urban sea with transferable insights to other urban seas in need of revitalization around the world.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".