Direct observations of density-driven streamwise oriented vortices at a river confluence
Bibliographic record
Abstract
When rivers collide, complex three-dimensional large-scale coherent turbulent structures are generated along the confluence’s mixing interface. These structures play important roles in mixing streamborne pollutants and suspended sediment, and have considerable bearing on the morphology and habitat quality of the postconfluent reach. A particular structure of great interest, streamwise orientated vortices (SOVs), were first detected in numerical simulations to form in pairs, one flanking each side of the mixing interface rotating in the opposite sense of the other. Since, it has proved difficult to detect SOVs with conventional pointwise velocimetry instrumentation. Despite the lack of empirical or observational evidence to confirm their existence and understand their dynamic behaviour, SOVs are nevertheless considered important drivers of mixing and sediment transport processes at confluences. Their causal mechanisms are also not fully understood, hindering progress towards a robust conceptual model of confluence turbulent mixing. To address these gaps, we present and analyse direct observations of highly dynamic and coherent SOVs captured in aerial drone video at a mesoscale confluence presenting a stark turbidity contrast between its tributaries. Eddy-resolved modelling demonstrates the dynamics of the SOVs can only be reproduced when a small density difference (Δρ) is imposed between the tributaries ( Δρ = 0.5 kg/m 3 ). Our results conclusively demonstrate that SOVs do exist and that a small difference in density between the tributaries inverts the sense of rotation of the SOVs and their vertical position within the water column, causing important effects on the confluence’s turbulent mixing regime.
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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 | 0.001 | 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) | 0.001 | 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".