Ecosystem-Based Adaptation to Protect Avian Species in Coastal Communities in the Greater Niagara Region, Canada
Bibliographic record
Abstract
Coastal communities are increasingly vulnerable to climate change and its effects may push coastal ecosystems to undergo irreversible changes. This is especially true for shorebirds with the loss of biodiversity and resource-rich habitats to rest, refuel, and breed. To protect these species, it is critical to conduct research related to nature-based Solutions (NbS). Through a scoping review of scientific literature, this paper initially identified 85 articles with various ecosystem-based adaptation (EbA) strategies that could help conserve shorebird populations and promote ecotourism. Of these 85 articles, 28 articles had EbA strategies that were examined, with some like coral reefs and mangroves eliminated as they were inappropriate for this region. The scoping review identified four major EbA strategies for the Greater Niagara Region with living shorelines and beach nourishment being the most suitable, especially when combined. These strategies were then evaluated against the eight core principles of nature-based solutions protecting shorebird as well as human wellbeing. Living shoreline strategy was the only one that met all eight NbS principles. As the coastline of the region greatly varies in substrate and development, further research will be needed to decide which EbA strategies would be appropriate for each specific area to ensure their efficacy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".