Investigation of 3D circulation and secondary flows in the St. Lawrence fluvial estuary at a tidal junction
Bibliographic record
Abstract
To enhance understanding of the complex functioning of the St. Lawrence fluvial estuary—a macro-tidal, freshwater estuary located in Quebec, Canada—a 3D numerical model is set up to investigate its hydrodynamics. Validation of the 3D model used field data on water levels, discharge rates, and velocities during both neap and spring tide periods. Comparison of the model with existing 2DH results illustrates the 3D model's ability to represent the time evolution of the secondary flow during tidal forcing in the confluence/divergence zone around Île d'Orléans. 3D results highlight the great importance of the vertical component of velocity in studying a site with complex geometry. A more detailed analysis of velocities and turbulence at the Île d'Orléans junction shows a time lag of around 1h between current slack and the tidal slack. On the one hand, the current reverses earlier at the bank level than in the deep channel during both ebb and flood periods. On the other hand, the current reverses more quickly at the bottom than at the surface in the main channel. Site geometry, friction and the presence of return currents are the main factors explaining this. This paper highlights the importance of 3D modeling for gaining a deeper understanding of estuarine dynamics, even in the tidal freshwater zone, revealing processes ignored by 2D depth integrated models. Such modeling can assist in planning future field measurement campaigns and improve space-time interpolation methods for velocities in wide estuaries. Additionally, it provides a solid foundation for studying couplings (chemical or particulate) and making predictions, particularly in the context of climate change. • New 3D numerical model of the St. Lawrence estuary. • Flow reversal and turbulence during flood/ebb cycle for spring/neap tide in a tidal junction. • Current recirculation and phase delay between shallow bank and deep channel.
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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".