Developing a Coastal Hazard Prediction System in Ice-Infested Waters, Part 1: High-Resolution Regional Wave Modeling in The Estuary and Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract. This study is the first of a two-part paper that summarizes the development of a prototype coastal hazard prediction system providing short-term (+48 h) forecasts of the total water level (TWL) at 50 m resolution for the province of Quebec, Eastern Canada. In this first part, the implementation of the offshore wave model component of the system, which is a regional 1 km-resolution WAVEWATCH III™(WW3) configuration for the Estuary and Gulf of St. Lawrence (EGSL), is presented and discussed. The configuration is forced by high resolution atmosphere, ocean and sea ice forecasts provided by Environment and Climate Change Canada (ECCC) and includes a state-of-the-art parameterization of wave propagation and attenuation in sea ice that has been tuned with observations from the EGSL. Performances are assessed against wave data collected over a two-year period during which the forecasting system was running operationally, and against historical storm data using a model hindcast. Results demonstrate reasonable forecast skills both for normal and extreme wave conditions during ice-free periods with errors ranging from 15 % to 31 % of the mean wave height. However, when sea ice is present, performances are drastically reduced, primarily due to inaccuracies in the predicted ice fields at spatial scales over which wave energy typically dissipates in sea ice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".