Assessing the impact of hurricane Fiona on the coast of PEI National Park and implications for the effectiveness of beach-dune management policies
Bibliographic record
Abstract
Abstract The impact of waves, storm surge, and aeolian transport associated with Post-tropical Storm Fiona (offshore significant wave height ∽ 8 m, storm surge up to 2 m) on the sandy beaches and foredunes of the north shore of Prince Edward Island National Park (PEINP), Canada, are assessed. Management policies and practices, as they apply to sandy beach systems within PEINP, are reviewed in the context of the shoreline changes attributed to Fiona. The effectiveness of these policies and practices are evaluated to inform the potential performance of beach-foredune systems as natural protection measures that mitigate the impacts of large-magnitude storms and relative sea-level rise (RSLR) on shoreline change. The analyses utilise survey data, ground photography, and unoccupied aerial vehicle (UAV) imagery collected before (October 2021 to July 2022) and after (October 2022 and May 2023) Fiona. In general, the largest dunes were characterised by erosion of the stoss slope, with landward retreat of the dune toe by < 6 m and minimal impact on crest height and position. Small foredunes (< 5 m in height) generally showed significantly greater erosion in terms of dune profiles, with dune breaching occurring at some locations. Foredunes perched on bedrock and till, which were typically smallest in size, were subject to complete erosion, thereby exposing the hard underlying surface. Overall, the impact of Fiona on sandy beach systems in PEINP was relatively modest in many locations, reflecting the success of existing management policies and practices that protect and maintain the integrity of foredunes by minimizing human impacts and avoiding ‘coastal squeeze’.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".