Drag Coefficient of Submerged Flexible Vegetation Patches in Gravel Bed Rivers
Bibliographic record
Abstract
Vegetation patches and strips either along riverbanks or in channel beds are essential for the protection of erosion and sedimentation processes. In the present study, the drag coefficient Cdv of submerged flexible vegetation patches in gravel bed rivers was investigated. A total of 13 vegetation patches with different densities were studied in disparate reaches of the Padena Marbor and Beheshtabad gravel bed rivers in Iran. Water depths, flow velocities, and particle grain sizes around these vegetation patches were collected. The Saint-Venant equation and various empirical equations for estimating the drag coefficient were applied to study hydrodynamics in the presence of vegetation patches under nonuniform flow conditions. Furthermore, the drag coefficient factor of flexible vegetation was used to represent the flexibility of vegetation patches and drag characteristics, which were explored from the perspective of material mechanics. The results showed that the calculated values of Cdv exhibited nonuniform variations with the increase in the Reynolds number along the streamwise direction due to flow nonuniformity. Two effects caused by flexible vegetation patches were observed, namely, the sheltering effect (for Red>580) and blockage effect (for Red<450). In most of the vegetated patches, the sheltering effect was dominant, which reduced the drag coefficient. Finally, a fitting formula was proposed based on the drag coefficient factor and Cauchy number.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".