Impact of Density Gradients on the Secondary Flow Structure of a River Confluence
Bibliographic record
Abstract
Abstract A small gradient in the densities (Δ ρ ) of two rivers in Canada was recently shown to develop coherent streamwise orientated vortices (SOVs) in their confluence. Here we use eddy‐resolved numerical modeling to examine how the magnitude and direction of Δ ρ affect these secondary flow structures. At equal density, lone anticlockwise SOVs are predicted near the surface, a possibility supported herein by recent aerial observations of such SOVs at the confluence. When a Δ ρ is considered, a front from the denser channel always slides underneath the lighter channel independent of whether the dense front pushes into the fast (Coaticook) or slow (Massawippi) tributary. When the fast Coaticook is denser, coherent clockwise rotating SOVs tend to form. However, when the slow Massawippi is denser, interfacial instabilities are generated as the fast flow of the Coaticook shears overtop the dense front. Thus, the tributary's velocity opposing the dense front's propagation modifies secondary flow characteristics. Importantly, both these cases would have the same densimetric Froude number ( F D ) if defined using an average velocity of the confluence (e.g., that of the downstream channel), yet this single F D value cannot account for the differences observed between both. This ambiguity inherent to current conventions for calculating F D places into question its use as an adequate predictor of density‐driven secondary flow at confluences.
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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 | 0.000 | 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".