Tidal plume internal wave pumping, mixing and cross shelf exchange of sediment and biota in a shallow river plume
Bibliographic record
Abstract
<!--!introduction!--> The Rhine River Plume forms one of the largest Regions of Freshwater Influence (ROFI) in Europe. The formation and evolution of the tidal plume fronts on every ebb tide, and the generation of internal waves ahead of the front were captured by the STRAINS (STRAtification Impacts Near-shore Sediment) field campaign off the Dutch coast. Here we explore the trapping of internal waves generated by multiple tidal plume fronts in the mid-field of the Rhine River Plume. Observations and radar images show that tidal plume fronts propagate all the way to the coast into 2 m of water, as well as their reflection and breaking. Using a hydrostatic model and the field data we explore the interaction of the tidal plume fronts, and relic tidal plume fronts in the near to mid-field plume. As the plume fronts propagate onshore they increase cross-shore mixing and also increase sediment resuspension. Using a non-hydrostatic numerical model we explore how the fronts generate high frequency internal waves, that break and mix as they propagate onshore. We describe a cross-shore frontal pumping mechanism, and show how this impacts near shore mixing, sediment resuspension and offshore transport. Tidal plume fronts are thicker and faster under downwelling winds, in contrast they are thinner and slower under upwelling winds. We consider how this changes during periods of extreme drought and floods. We consider similarities and differences to other river plumes, and how we can apply knowledge from highly sampled river plumes, to remote river plume 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".