Computational optimization in simulating velocities and water-surface elevations for habitat–flow functions in low-slope rivers
Bibliographic record
Abstract
The impact of varying computational mesh discretization on the accuracy of simulating velocities and water-surface elevations was investigated using the River2D model and data from 10 study reaches in three low-slope (<0.2%) Canadian rivers. A wide range of computational aspects were examined, including node spacing (1.35 to 60 m), number of mesh nodes (1250 to 58,000) and domain widths (60 to 800 m). Computed values for average cross-sectional velocities and water-surface profiles were compared with corresponding field survey data. The statistical mean absolute and root-mean-square errors were used to evaluate the discrepancy between measured and simulated values due to the mesh discretization characteristics. The results showed that mesh design discretization had a pronounced effect on the precision of the velocity and water-surface elevation predictions. Although the ratio of node spacing to river reach width may be site specific, it was found that optimal values should be <0.022 to obtain the highest accuracy. Regression equations of optimal ratios of node spacing to river reach width and the corresponding minimum mesh resolution errors were estimated. An example is provided that illustrates how the choice of computational mesh properties can affect habitat characterization for fish.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".