Augmenting two-dimensional hydrodynamic simulations with measured velocity data to identify flow paths as a function of depth on Upper St. Clair River in the Great Lakes basin
Bibliographic record
Abstract
Upper St. Clair River, which receives outflow from Lake Huron, is characterized by flow velocities that exceed 7 feet per second and significant channel curvature that creates complex flow patterns downstream from the Blue Water Bridge in the Port Huron, Michigan, and Sarnia, Ontario, area. Discrepancies were detected between depth-averaged velocities previously simulated by a two-dimensional (2D) hydrodynamic model and surface velocities determined from drifting buoy deployments. A detailed ADCP (acoustic Doppler current profiler) survey was done on Upper St. Clair River during July 1–3, 2003, to help resolve these discrepancies. As part of this study, a refined finite-element mesh of the hydrodynamic model used to identify source areas to public water intakes was developed for Upper St. Clair River. In addition, a numerical procedure was used to account for radial accelerations, which cause secondary flow patterns near channel bends. The refined model was recalibrated to better reproduce local velocities measured in the ADCP survey. ADCP data also were used to help resolve the remaining discrepancies between simulated and measured velocities and to describe variations in velocity with depth. Velocity data from ADCP surveys have significant local variability, and statistical processing is needed to compute reliable point estimates. In this study, velocity innovations were computed for seven depth layers posited within the river as the differences between measured and simulated velocities. For each layer, the spatial correlation of velocity innovations was characterized by use of variogram analysis. Results were used with kriging to compute expected innovations within each layer at applicable model nodes. Expected innovations were added to simulated velocities to form integrated velocities, which were used with reverse particle tracking to identify the expected flow path near a sewage outfall as a function of flow depth. Expected particle paths generated by use of the integrated velocities showed that surface velocities in the upper layers tended to originate nearer the Canadian shoreline than velocities near the channel bottom in the lower layers. Therefore, flow paths to U.S. public water intakes located on the river bottom are more likely to be in the United States than withdrawals near the water surface. Integrated velocities in the upper layers are generally consistent with the surface velocities indicated by drifting-buoy deployments. Information in the 2D hydrodynamic model and the ADCP measurements was insufficient to describe the vertical flow component. This limitation resulted in the inability to account for vertical movements on expected flow paths through Upper St. Clair River. A three dimensional hydrodynamic model would be needed to account for these effects.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".