Recirculation and trapping of tidal plume fronts and internal wave generation in the Rhine ROFI
Bibliographic record
Abstract
River plumes dominate transport and mixing in our coastal oceans. We present data and introduce a conceptual picture showing how a multi-frontal, mid-field river plume can form, and how the newly released tidal plume fronts can generate multiple internal solitary waves. We focus on the analysis of data and 3D modelling of the Rhine ROFI, and discuss how a recirculation zone forms in the near to mid-field plume. Our data show the presence of tidal plume fronts, as well as relic tidal plume fronts. It also shows two internal solitary wave packets ahead of a new tidal plume front. Tidal advection and a strong recirculation is found to trap tidal plume fronts within 20 km from the river mouth. This trapping maintains the multi-frontal system. We find that when tidal straining is large, the tidal plume fronts re-strengthen due to increased convergence. The recirculation zone is populated by multiple tidal plume fronts that return to the near field plume, and subsequently interact with the newly forming tidal plume front that discharges freshwater on the ebb tide. Understanding near-to mid-field river plume systems is important for our understanding of along and cross-shore exchange and mixing. Ocean models typically do not resolve non-hydrostatic internal solitary waves, we discuss how this may limit our understanding of mixing in near to mid-field river plumes.
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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".