A modeling study of the impact of major storms on waves, surface and near‐bed currents on the <scp>G</scp>rand <scp>B</scp>anks of <scp>N</scp>ewfoundland
Bibliographic record
Abstract
Abstract Waves and current processes, both surface and near‐bed were simulated for major storms on the Grand Banks of Newfoundland using integrated wave, 3‐D tidal and ocean current models. Most storms track southwest to northeast and pass to the north or northwest of the Grand Banks. Significant wave heights can reach up to ∼14 m and are predominantly to the northeast at the peak of storms. Extreme surface currents reach approximately 1 m s−1 and are largely to the southeast. The strongest bottom currents, up to 0.8 m s−1, occur on St. Pierre Bank and are dominantly to the south and southeast. While wave height and wind‐driven current generally increase with wind speed, factors such as storm paths, the relative location of the storm center at the storm peak, and storm translation speed also affect waves and currents. Surface and near‐bed wind‐driven currents both rotate clockwise and decrease in strength as the storm traverses the Grand Banks. While the spatial variability of the storm impact on surface currents is relatively small, bottom currents show significant spatial variation of magnitude and direction as well as timing of peak current conditions. These spatial variations are controlled by the changes of bathymetry and mixed layer depth over the model domain. The storm‐generated currents can be 7 to 10 times stronger than the background mean currents. These strong currents interact with wave oscillatory flows to produce shear velocities up to 15 cm s−1 and cause wide occurrences of strong sediment transport over nearly the entire Grand Banks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".