Impacts of pool and vegetated banks on turbulent flow characteristics
Bibliographic record
Abstract
Bed forms and vegetation significantly influence flow characteristics. Macro-scale bed forms, such as pools, result in non-uniformity in flow; vegetation alters turbulence intensity, shear stress, and velocity distributions. To date, limited work on impacts of pools and vegetated banks on flow characteristics has been reported. Based on experiments carried out in laboratory, this study investigates the effects of pool and vegetated banks on flow velocity, Reynolds stress and turbulence intensity distributions. Results for a channel with pool and vegetated banks (vegetated-bank pool channel) are compared to those for channel with pool but without vegetation on pool banks (bare-bank pool channel). Results show that the combined impacts by vegetated banks and pools will intensify dip phenomenon and change the flow structure and its characteristics. It is also found that near both the pool entry and vegetated banks, estimation of the roughness coefficient is affected by the complex flow conditions. As a consequence, it is difficult to apply the Reynolds-averaged Navier-Stocks equations to interpret the results.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".