Assessing the suitability of living shoreline 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. \nWave action continuously works on the unconsolidated till and sandstone banks, eroding and \nreceding coastlines, while nourishing beaches and estuaries. This causes intensified risk to \nproperties, infrastructure, and humans. Hard engineered structures are common, short-term, \nsolutions to coastal erosion. These structures disrupt the natural land-water interaction as wave \nenergy is deflected at the structure and dispersed to adjacent areas, increasing erosion. Living \nshorelines are used as alternatives to hard structures by incorporating natural materials, such as \nvegetation, to provide coastal protection. Living shorelines are long-term methods for coastal \nerosion. These adaptations act as wave energy barriers and sediment traps, slowing erosion rates. \nCertain characteristics and baseline conditions such as vegetation, geology, geomorphology, \nsediment, and differing exposure types are required for living shoreline techniques to reap their \nintended benefits. Tools to assess site suitability for living shorelines, available online or through \ndocuments, are critiqued. The critique is based on how well the tool characterizes PEI, signifying \nhow useful it would be if used as an assessment resource for the suitability of living shorelines. \nA multicriteria evaluation was conducted in ArcGIS Pro to identify segments of the shoreline \nthat were suitable, moderately suitable, and unsuitable for implementing living shorelines. This \nmodel was tested using 31 field sites surveyed between July-August 2021. With this information, \ngovernments and coastal property owners will be able to determine whether or not their property \nwould benefit from installation of living shorelines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".