Local and Remote Storm Surge Contributions to Total Water Levels in the Gulf of St. Lawrence During Hurricane Fiona
Bibliographic record
Abstract
Abstract Post‐tropical Hurricane Fiona generated a large storm surge that resulted in pronounced flooding and coastal erosion in Atlantic Canada in September 2022. In this study we apply a regional barotropic storm surge model in the Gulf of St. Lawrence, a semi‐enclosed sea, to demonstrate a method of evaluating different contributions to the total water levels. These include the surge generated over the ocean, the surge generated by the cyclonic winds over the gulf, and the tides. The results indicate that the highest storm surge occurred in the southeastern region, a combination of locally and remotely generated components. The surge that entered from the ocean was greater than the surge generated over the gulf; however, these were not in phase. To investigate the case where the local and remote surges are coincident, we shift the wind field relative to the timing of the boundary conditions and find the near “perfect storm” with significantly higher storm surge elevations. These findings highlight the importance of basin morphology and storm conditions in controlling the interactions of surge components, and this approach can be applied to simulate a range of storm‐driven hazard outcomes for future extreme events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".