The impact of Hurricane Fiona on sandy beaches and foredunes in Prince Edward Island National Park: Implications for management.
Bibliographic record
Abstract
This study investigates the impact of Hurricane Fiona on sandy beaches and foredunes within Prince Edward Island National Park (PEINP). Fiona was the strongest storm to strike the island in nearly a century, with significant wave heights reaching 8 metres. Its impact on sandy beach-dune systems provides an opportunity to gauge the effectiveness of current PEINP's management policies and practices, and to consider potential changes that enhance the role of foredunes and beaches as natural defences against future storms and rise in relative sea level.Survey data and ground/UAV photography were used to compare various locations before (October 2021 to July 2022) and after (October 2022 and May 2023) the storm. High dunes experienced stoss slope erosion without significant changes in the height or position of the foredune crest, offering protection to landward areas. Low dunes were substantially eroded, leading to overwash in certain areas, and dunes located on bedrock and till were completely eroded, exposing the underlying surface. Hurricane Fiona's impact highlights the need of reinforcing current management strategies in PEINP that aim at safeguarding the natural biotic and abiotic components of beach-dune systems, and securing the accommodation space needed for their natural inland migration with rising sea level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".