Mixing dynamics at river confluences governed by intermodal behaviour
Bibliographic record
Abstract
Abstract The extent to which flows mix at confluences is pivotal for determining spatial patterns of water quality and biodiversity. Because mixing processes are complex, predicting rates and characteristic scales of mixing is difficult. Here we introduce a theory for confluence mixing dynamics of shallow flows in which the mixing process is controlled by the switching between two modes of behaviour—one similar to a wake behind an obstacle and the other similar to a mixing layer between two parallel flows. Dye visualizations of mixing in field-based controlled experiments agree with theoretical predictions and support transitional behaviour between the two mixing modes. According to our theoretical framework, the mixing interface can grow rapidly in wake mode, when large vortices are shed from a zone of stagnant flow within the confluence, but lateral shear between incoming flows is negligible. This rapid growth occurs even though flow curvature and shallowness inhibit growth through advective and turbulent lateral exchange of momentum and through bed friction. Our findings provide insight into the importance of different modalities of flow structure in controlling mixing at river confluences, thereby contributing to practical knowledge on the role of confluences in dispersal of contaminants in river systems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".