Dynamics of a river channel confluence with discordant beds: Flow turbulence, bed load sediment transport, and bed morphology
Bibliographic record
Abstract
River channel confluences play a major role in the dynamics of all fluvial systems, and yet our understanding of bed load routing at these sites is very sparse. The dynamics of confluences are a function of the momentum ratio between the combining flows and the three‐dimensional geometry of the junction. Recent experiments have shown that discordance in bed height between the confluent rivers increases turbulence intensity and enhances upwelling of flow within the confluence. However, the significance of these flow characteristics on sediment transport is still unknown. To examine the relations between flow and sediment transport, we have measured near‐bed flow turbulence, bed load transport rates, and changes in bed morphology for eight different flow conditions at a sand bed discordant confluence. Detailed analysis of the near‐bed flow patterns reveals that within the shear layer, low mean flow velocities are combined with the highest values of Reynolds shear stresses and that turbulence generation is associated with intense upward movements of flow. High sediment transport rates are found at the edges of the shear layer region where horizontal‐vertical cross stresses ( ρ Uw′) are high. These patterns match changes in bed morphology where erosion occurs along the shear layer. The relation between the shear layer and sediment transport confirms the role of bed discordance on the dynamics of the confluence. Migration of the shear layer into the confluence, as a result of a change in momentum ratio, modifies local near‐bed flow characteristics, sediment transport rates, and the spatial distribution of deposition and erosion zones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".