Characteristics of Shallow Flows in a Vegetated Pool—An Experimental Study
Bibliographic record
Abstract
Pools are often observed in gravel-bed rivers, together with the presence of vegetation patches. In the present study, a conceptual model of a gradual varied flow with both convective deceleration and acceleration flow sections has been constructed in a flume to study turbulent flow structures. Vegetation patches with extended canopies were planted in the pool sections in order to increase the thickness of the boundary layer inside the inner zone. The effects of different flows (namely decelerating, uniform and accelerating flows) along an artificial pool on flow velocity, shear stress and bursting events have been investigated. In addition, due to the occurrence of secondary currents in shallow streams, the characteristics of turbulent shallow flow have been investigated along two axes that are parallel to the sidewall of the flume. The results showed that the application of the log law should be used with care to estimate shear velocity along a pool with a vegetated bed. The presence of a vegetation patch causes an increase in Reynolds shear stress, especially along the entrance section of the pool where the flow decelerates. The results of the quadrant analysis reveal that the sweep and ejection events have the most dominant influence over the vegetation patch in the pool; however, the contributions of outward and inward events increase near the bed, especially in the entrance section of the pool where the flow is decelerating. The distribution of stream-wise RMS of turbulence intensity along the pool generally presents a convex shape.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".