Citizen science monitoring of beach and dune erosion during Hurricane Fiona
Bibliographic record
Abstract
Hurricane Fiona made landfall as an extra-tropical storm along the north shore of Prince Edward Island (PEI), Canada, in October 2022. The state of the beach and dune immediately before and after the storm was captured through the Coastie citizen science beach and dune photo monitoring initiative, as part of the global CoastSnap Community Beach Monitoring program. Coastie monitoring sites within Prince Edward Island National Park (PEINP) revealed extensive dune scarping, capturing a 12–17 m retreat of the foredune at Brackley and Cavendish Beaches. Using foredune scarp and post-storm shoreline positions, volumetric losses between 28 and 76 m3 m−1 are estimated from profiles located within the first 150 m of the stations. The average horizontal position of the projected foredune scarp position was within 2.8 m of the position identified from high accuracy unoccupied aerial system (UAS) surveys, corresponding to a mean absolute difference of 15.3 m3 m−1 or 45.3% for dune volume changes estimated from the images. Continued monitoring will yield further improvements to the volume loss estimation methodology, and insight on the timing and mechanisms of beach and dune recovery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".