Top 10 Principles for Designing Healthy Coastal Ecosystems Like the Salish Sea
Bibliographic record
Abstract
Like other coastal zones around the world, the inland sea ecosystem of Washington (USA) and British Columbia (Canada), an area known as the Salish Sea, is changing under pressure from a growing human population, conversion of native forest and shoreline habitat to urban development, toxic contamination of sediments and species, and overharvest of resources. While billions of dollars have been spent trying to restore other coastal ecosystems around the world, there still is no successful model for restoring estuarine or marine ecosystems like the Salish Sea. Despite the lack of a guiding model, major ecological principles do exist that should be applied as people work to design the Salish Sea and other large marine ecosystems for the future. We suggest that the following 10 ecological principles serve as a foundation for educating the public and for designing a healthy Salish Sea and other coastal ecosystems for future generations: (1) Think ecosystem: political boundaries are arbitrary; (2) Account for ecosystem connectivity; (3) Understand the food web; (4) Avoid fragmentation; (5) Respect ecosystem integrity; (6) Support nature's resilience; (7) Value nature: it's money in your pocket; (8) Watch wildlife health; (9) Plan for extremes; and (10) Share the knowledge.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".