Resilient Coasts for Salmon - Empowering Communities with Nature-based Solutions to Adapt to Climate Change
Bibliographic record
Abstract
Coastal communities in the Salish Sea are facing unprecedented challenges as climate change continues to evolve. A five-year project collaboration between the Pacific Salmon Foundation and the Stewardship Centre for BC, called Resilient Coasts for Salmon: Nature-based Solutions for Climate Change (RC4S), is working to empower citizens by providing nature-based solutions that encourage resiliency for coastal communities and ecosystems. Utilizing a multifaceted approach, RC4S is raising public awareness about climate change impacts in local South and East Coast Vancouver Island communities and nature-based solutions to help adapt to those threats. RC4S is also building professional capacity in shoreline restoration through Green Shores® training, and creating opportunities for community members to learn about their local shorelines through a citizen science mapping project and Green Shores demonstration sites. The demonstration sites will also create visibility for nature-based solutions in action and will be developed in partnership with First Nations and other local governments such as the K’ómoks First Nation, Comox Valley Regional District, Capital Regional District, as well as stewardship groups and organizations, such as World Wildlife Fund Canada, and Peninsula Streams. All project elements are intended to address the findings from a preliminary survey showing that although the majority (74%) of local Vancouver Island community members are concerned about climate change, most (79%) had only minimal or moderate knowledge of what the predicted climate change impacts are for communities along the Salish Sea. The underlying vision for the project is to empower citizens to develop resiliency to climate change, now and into the future.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".