Habitat Recovery in the Salish Sea, One Community at a Time: Community engagement for socio-ecological resilience of coastal restoration projects
Bibliographic record
Abstract
Community engagement builds both social and ecological resilience of restoration projects. This is particularly true in coastal areas, which are complex both ecologically and socially. The Salish Sea Nearshore Habitat Recovery Program builds on 20 years of community building towards coastal ecosystem restoration on the south coast of British Columbia, Canada. Funding was obtained by a small non-profit organization for seagrass and marine riparian restoration, and marine debris removal to support forage fish and juvenile salmon habitat recovery. The engagement and involvement of a broad community including Indigenous groups, coastal residents, all levels of government, academics and industry has resulted in the equivalent of tens of thousands of dollars of additional support for restoration works within this program. The broader community is engaged in multiple steps of restoration including site selection, planning and implementation. Recognition of the value of traditional and local knowledge, an investment in bringing people together and ongoing communication have built collective enthusiasm and ownership over the project. Marrying restoration activities with cultural events is increasing ecological literacy and building the resilience of the work. Restoring or establishing connections between parties that do not otherwise work together is a fundamental step towards restoring ecosystems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".