Advective Lateral Transport of Streamwise Momentum Governs Mixing at Small River Confluences
Bibliographic record
Abstract
Abstract Confluences are important sites for mixing within river networks. Past work has shown that mixing within confluences is highly variable; in some cases flows mix rapidly and in other cases flows remain unmixed far downstream of the confluence. The fluvial processes that govern mixing within confluences remain poorly understood. This study relates patterns and amounts of mixing to three‐dimensional flow structure at three small confluences. It focuses on lateral fluxes of streamwise momentum, which theoretical considerations suggest should influence lateral mixing. Patterns and amounts of mixing differ at the three sites. Considerable mixing occurs at an asymmetrical confluence with strong helical motion within flow from the lateral tributary, which produces substantial differences in advective lateral transport of streamwise momentum over depth. Minor mixing occurs at a comparatively symmetrical confluence where incoming flows have relatively equal momentum fluxes; however, helical motion within one of the flows locally increases mixing. At a symmetrical confluence where one incoming flow has much greater momentum flux than the other, mixing occurs largely through progressive lateral shifting of the mixing interface toward the minor tributary because of the strong lateral flux of streamwise momentum by the dominant tributary. At all three confluences, lateral turbulent transport of streamwise momentum is an order of magnitude less than advective lateral transport of streamwise momentum. The study indicates that generalization of mixing at confluences remains challenging but that advective lateral fluxes of streamwise momentum related to secondary currents (helical motion) or primary flow (cross currents) greatly enhance mixing at confluences.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 teacher head, 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".