Impacts of Extratropical Cyclone Fiona on a sensitive coastal lagoon ecosystem
Bibliographic record
Abstract
Abstract Oceanic storms can strongly disturb the physical and biogeochemical conditions of transitional coastal waters. Impacts of extreme oceanic storms on coastal ecosystems have received limited attention worldwide, with no studies at higher latitudes (> 45°) where tropical cyclones have usually abated. This study investigates the combined impacts from marine and atmospheric forcing on a coastal lagoon in Prince Edward Island, Canada, during and after Extratropical Cyclone Fiona in September 2022. Physical (water levels and temperature) and biogeochemical (dissolved oxygen [DO], electrical conductivity, pH, nitrate–nitrogen concentrations, total suspended solids [TSS]) datasets from the lagoon and the watershed's tributaries, groundwater springs, and piezometers were used to assess ecosystem disturbance and recovery timelines following the storm. Fiona resulted in a 1.6 m storm surge into the lagoon that elevated water temperatures by up to 6°C, disturbed the density‐dependent stratification of salinity and temperature, and reduced the diel amplitude of DO, indicating a reduction in plant respiration due to ecosystem disturbance. The freshwater tributaries revealed sharp changes in flow (30‐fold increase), nitrate–nitrogen (NO 3 ‐N) concentrations and loading (70‐fold increase), and TSS loading (40‐fold increase) to the lagoon during and immediately following the storm. The lagoon rapidly recovered (hours) from the hydraulic disturbance of the storm surge, but elevated nutrient levels persisted for months. The intensity and frequency of extratropical cyclones is projected to increase in the Northwest Atlantic, making field‐based studies of cyclone impacts on coastal waters critical for understanding future coastal ecosystem disturbance and recovery periods relative to the timing of future storms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".