Controlling Factors and Dynamic Mechanisms of MultiDecadal Changes of Suspended Sediment Concentration in the Bohai Sea
Bibliographic record
Abstract
Abstract Global climate change and human activities jointly drive the long‐term changes of suspended sediment concentration (SSC) in the ocean, including high turbidity coastal seas such as the semienclosed Bohai Sea. In the past few decades, SSC in the Bohai Sea has exhibited a significant decreasing trend according to satellite observations. In this study, a regional numerical model is employed to carry out sensitivity experiments to quantify the relative contributions of several controlling factors to this multidecadal SSC decline in the Bohai Sea. The model results suggest that reduction in wind speed (by 20% over the past 40 years) can cause the largest decrease (8 mg L −1 , 32%, with values close to satellite remote sensing data) in SSC averaged over the Bohai Sea, associated with a decrease (17%) of wave‐induced bottom shear stress. The coastline and bathymetry changes, due mainly to sediment discharge of the Yellow River, cause large SSC changes at local scales. Since 1976 when the Yellow River mouth was relocated from Bohai Bay to Laizhou Bay, sediment deposition led to significant shoaling in the new estuarine area. This can increase the bottom shear stress by over 50% and the local SSC by more than 30 mg L −1 and an increase (2 mg L −1 , 8%) in SSC over the Bohai Sea. The quantification of contributions from different controlling factors can help to predict long‐term SSC changes in the Bohai Sea, and the method of this study can be applied to other coastal regions where such quantifications are still lacking.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".