Simulation of Natural River Flow by a Three-Dimensional Hydrodynamic Model
Bibliographic record
Abstract
A fully calibrated three-dimensional hydrodynamic model, namely Curvilinear Hydrodynamics in 3-Dimensions (CH3D), has been developed to determine changes of flow velocity in a natural river. The objective of this paper is to present the application of the CH3D modeling. A grid scheme was constructed by using SMS software along an example river-the Detroit River from Lake St. Clair to Lake Erie with two natural curved banks as the transversal boundaries. The water depth (vertical σ-) was divided into several layers from water surface to the river bottom. A modification to the program was made by the authors to enable flexible Manning's roughness by applying the Strickler's formula. The National Oceanic and Atmospheric Administration (NOAA, USA) bathymetry data were post-processed for water level corrections by using Maplnfo software. The data were also extended to areas where there were no NOAA measurements in the Detroit River basin. Modeling results show that computed velocities and velocity measurements at various river cross-sections are in a good agreement typically below 10% error. Comparison between the computational water surface elevations versus that of records in gauge stations along the Detroit River is also in a good agreement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".