Enhancing Climate Resiliency Through Improving Ecosystem Services in Shoreline Municipalities – Lessons from Canada
Bibliographic record
Abstract
The accelerated impacts of climate change in waterfront areas and the proven inefficacy of the aging hardened shoreline infrastructure have driven shoreline management practices to evolve toward the enhancement of ecosystem services at the land-water interface. Gaining momentum as an adaptive approach in regenerative projects, living shorelines are comprised of natural ecosystem components used in combination or in place of traditional hard engineering methods to provide coastal protective services and erosion mitigation. The success of living shorelines in protecting shoreline property and ecosystem integrity varies based on the biogeomorphology and hydrology of the region and is also heavily reliant on social acceptance of the chosen approach and best practice for implementation. The relatively lower lifecycle cost and associated co-benefits of living shorelines have well positioned them as a promising alternative approach in theory. There are, however, gaps in regional long-term datasets and evidence-based guidelines. This research provides an overview of the underlying geopolitical readiness for integrating nature-based solutions in climate adaptation strategies within shoreline municipalities based on a comprehensive literature review complimented by expert interviews. The synthesized data can inform decisions for minimizing the destructive effects of traditional shoreline erosion prevention approaches and encourage successful implementation of solutions that offer ecological, health, social, and economic benefits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".