Hydrodynamics, Sediment Transport and Morphological Features at the Confluence Between the Yangtze River and the Poyang Lake
Bibliographic record
Abstract
Abstract Confluences act as critical nodes in a river network as they affect flow, sediment transport, water quality, and ecological patterns. A complete knowledge about hydro‐morpho‐sedimentary processes at river confluences is still incompleted and it has been usually accepted that secondary flows are weak because of the significant role of form roughness in large rivers. In this study, two field surveys were conducted on the flow structure, suspended sediment transport and morphology of the confluence between the Yangtze River (the largest river in China) and the Poyang Lake (the largest freshwater lake in China). Dual counter‐rotating cells were observed during high flow conditions and a single secondary cell appeared in low flow conditions. These helical cells restricted the core size of high sediment concentration and downwelling flows acted as a barrier hindering the exchange of sediment between the two rivers. Furthermore, the observed large scour hole was likely related to the downwelling and upwelling flows caused by helical motions. In low flow conditions, the scour hole looked like a deep channel, which was likely related to a long‐surviving helical cell. The scour hole disappeared further downstream, when either the helical motion got weak during low flow conditions, or when a reverse helical cell occurred during high flow conditions. Hydrodynamics, suspended sediment transport and morphological features observed at such a large confluence demonstrated that river planform geometry and discharge ratio affected the flow structure, especially the helical motion. This in turn affected sediment transport as well as the local bed morphology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".