Forecasting storm surges along the east coast of Canada and the North‐Eastern United States: The storm of 21 January 2000
Bibliographic record
Abstract
Abstract A powerful storm passed over the coastal waters of eastern Canada on the 21 and 22 January 2000 causing significant damage to coastal infrastructure. The storm generated a large (>1.4 m) storm surge in the southern Gulf of St. Lawrence that unfortunately coincided with a high spring tide. This resulted in record high water levels in the southern Gulf of St. Lawrence (e.g., the highest level at Charlottetown since records began in 1911) and severe flooding around Prince Edward Island and along the eastern shore of New Brunswick. During January 2000, a recently developed storm surge forecast system was running in pre‐operational mode at Dalhousie University. The core of the forecast system is a depth‐averaged, non‐linear, barotropic ocean model driven by forecast winds and air pressures produced by the Canadian Meteorological Centre's regional atmospheric forecast model. In this study we assess the forecast skill of the surge model for the 21 January storm by comparing its 24‐hour forecasts with two independent hourly dataseis: (i) sea levels recorded by 12 tide gauges located in eastern Canada and the north‐eastern United States, and (ii) depth‐mean currents recorded by an acoustic Doppler current profiler deployed on the outer Scotian Shelf. Overall, the forecasts of coastal sea level and depth‐mean currents are reasonable and have forecast errors below about 0.1 m and 0.1 m s−1 respectively.
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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 | 0.001 |
| 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".