Experiments on the Morphodynamics of Open Channel Confluences: Implications for the Accumulation of Contaminated Sediments
Bibliographic record
Abstract
Abstract Contaminated sediments are common in river networks. The flow convergence and particular flow structures in confluences, such as the flow separation zone, may result in greater accumulation of contaminated sediments than in other river locations, but this issue is rarely studied. In addition, the contaminated sediment transport is driven by the particular morphodynamics occurring at confluences. This article describes a novel confluence flume experiment on both morphodynamics and deposition patterns of contaminated sediments as a function of geometric and flow conditions. The initial equilibrium bed geometry was developed from a mobile bed and then fixed, allowing for subsequent flow velocimetry and sediment feeding. Colored sediments of fine gradation, which mimic contaminated sediments, were then fed to the tributary channel. The results suggest that the junction angle primarily determines the confluence bed morphology and sediment transport pattern while the discharge ratio is a secondary factor. It was also observed that most introduced sediments tended to deposit immediately after the cessation of feeding at the stoss of the bar in the flow separation zone. The time history of the transport of the contaminated sediments was also investigated. Sediment that initially deposited at the stoss of the bar eventually moved to the lee of the bar and deposited around the bar downstream of the confluence, demonstrating that the sedimentation pattern evolved to a state similar to the equilibrium 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".