Effect of a Mesoscale Eddy on Surface Turbulence at the Kuroshio Front in the East China Sea
Bibliographic record
Abstract
Abstract Field observations were conducted along three west‐east transects in the East China Sea Kuroshio Front in July 2017. The microstructure observations results show that the surface turbulence kinetic energy (TKE) dissipation rates in the northern and southern transects are about 2 orders larger than those in the middle transect. The sea surface temperature, mixed layer depth, and the submesoscale motions (with horizontal scales smaller than 30 km) also suggest the surface turbulences are more active in the northern and southern transects. The current velocity data and sea level anomaly show that the middle transect was located at the northern edge of a cyclonic eddy. The eddy‐generated cross‐front geostrophic current was observed to flow toward the opposite direction of the cross‐front Ekman transport and believed to counteract the Ekman buoyancy flux (EBF)‐induced turbulence enhancement. The TKE dissipation rates can be well scaled by a scaling considering the EBF and the wind in the northern and southern transects but wind scaling only in the middle transect, suggesting the EBF does not contribute to the surface turbulence in the middle transect. An improved empirical scaling considering the cross‐front geostrophic current is defined and can scale the TKE dissipation rates better, indicating an eddy plays a key role in surface turbulent mixing in the East China Sea Kuroshio front.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".