A Process Study of Seiches over Coastal Waters of Shenzhen China after the Passage of Typhoons
Bibliographic record
Abstract
Analysis of sea-level observations demonstrates that Typhoons Mawar (2017) and Mangkhut (2018) induced seiches in both Dapeng Bay and Daya Bay near Shenzhen of China, with periods varying from about 3.5 to 4.0 h. Typhoon Mawar (2017) also generated seiches with a period of about 1.2 h. Seiches with such periods in the two bays have not been reported in the past. In this study, we investigate the main processes affecting seiches over these coastal waters using a nested-grid ocean circulation modeling system. The modelled results of typhoon-induced seiches agree well with observations, which indicates that the seiches after the passage of typhoons are dynamically free waves generated by the storm-induced accumulation of water bodies in the two bays. Model sensitivity experiments show that wind directions have an important influence on the type and characteristics of seiches. When the wind stress causes the water body to accumulate in a cross-bay direction, seiches in a closed water body are generated. When the wind stress causes the water body to accumulate in an along-bay direction, seiches in a semi-closed water body are produced. Because of the irregularity of the bathymetry and coastline and variability of wind directions, these two types of seiches can exist simultaneously in the two bays.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".