Two‐layer tidal modeling of the Yellow and East China Seas with application to seasonal variability of the M<sub>2</sub> tide
Bibliographic record
Abstract
The baroclinic response of tide and tidal currents in the Yellow and East China Seas is investigated using a two‐layer numerical model. Seasonal variability in the M2 tide, especially the smaller summer amplitudes prevailing along the Korea/Tsushima Strait [ Kang et al., 1995 ], is investigated by a series of numerical experiments with varying degrees of stratification specific to winter and summer. Model results show that the summer amplitudes of the M2 tide around the southwestern tip of the Korean peninsula and Korea/Tsusima Strait decrease, with a peak decrease of about 14 cm off the southwestern tip of the Korean peninsula, while the summer amplitudes in other coastal regions tend to increase. This seasonal variability generally coincides with the observations. These models results indicate that seasonal stratification has several noticeable effects on the tides, including varying degrees of current shear, varying frictional dissipation, and varying barotropic energy flux. In particular, it drives complicated seasonal variability in the M2 tide, with a peak amplitude modulation of nearly 5% off the southwestern tip of the Korean peninsula. The seasonal variation of barotropic M2 energy flux through the eastern entrance of the Yellow Sea is thought to induce the corresponding variability in the M2 amplitude in the Korea/Tsusima Strait, with smaller amplitudes found in the summer.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".