Numerical Modelling of the Grand River Plume in Lake Erie during Unstratified Period
Bibliographic record
Abstract
Abstract The Grand River is a major contributor of nutrients and dissolved and suspended solids to the eastern basin of Lake Erie. To better understand the impact of the Grand River plume on the surrounding receiving waters, we integrated data analysis and modelling of the Grand River plume transport in the eastern basin of Lake Erie using a high-resolution depth-integrated nonlinear barotropic finite element model. An extended domain of receiving waters with closed boundary was applied in this numerical study due to the lack of observations needed for specifying the open boundary conditions. The size of closed domain was chosen by considering balance between the computing time and stabilizing the hydrodynamic flow. Numerical simulations of the influence of wind on the plume transport in the vicinity of the Grand River mouth were performed. The root mean square error values of alongshore and cross-shore current components were 5 and 2.85 cm s-1, respectively. The transport simulations compare favorably (±20%) with observations of conductivity in the vicinity of the Grand River mouth. This study demonstrates that a two-dimensional numerical model can reasonably predict the river plume transport in a large lake during unstratified periods. Plume movement is primarily controlled by the wind-driven coastal current. Our simulations indicate that the frequent reversals of this current should effectively limit the plume's alongshore extent and may result in a continuous coastal band of turbid water extending alongshore in either direction in the vicinity of the river mouth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".