Assessing the suitability of nature-based adaptation techniques for coastal erosion in Prince Edward Island, Canada
Bibliographic record
Abstract
Coastal erosion rates on Prince Edward Island (PEI) are increasing due to climate change. Wave action continuously works on the unconsolidated till and sandstone banks, eroding and receding coastlines. This causes intensified risk to properties, infrastructure, and humans. Common hard engineered structures are short-term solutions to coastal erosion. These structures disrupt the natural land-water interaction as wave energy is deflected at the structure and dispersed to adjacent areas, increasing erosion. Nature-based adaptations are used as alternatives to hard structures by incorporating natural materials, such as vegetation, to provide coastal protection. Nature-based adaptations are long-term methods for coastal erosion. These adaptations act as wave energy barriers and sediment traps, slowing erosion rates. Certain characteristics and baseline conditions such as vegetation, geology, geomorphology, sediment, and differing exposure types are required for nature-based adaptation techniques to reap their intended benefits. Tools to assess site suitability for nature-based solutions, available online or through documents, are critiqued. The critique is based on how well the tool characterizes PEI, signifying how useful it would be if used as an assessment resource for the suitability of nature-based adaptations. A multicriteria evaluation was conducted in ArcGIS Pro to identify segments of the shoreline that were suitable, moderately suitable, and unsuitable for implementing nature-based solutions. This model was tested using 31 field sites surveyed between July-August 2021. With this information, governments and coastal property owners will be able to determine whether or not their property would benefit from installation of nature-based adaptations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".