Flow and turbulence redistribution in a straight artificial pool
Bibliographic record
Abstract
Multiple hypotheses have been advanced to explain the occurrence of pools in gravel bed rivers. These hypotheses were developed without a hydrodynamic model of how open channel flow is affected by pools, and it is not clear why and when the flow phenomena they describe might occur. Laboratory experiments are warranted to improve our understanding of how a gradual convective deceleration and acceleration of the flow, without flow separation, redistributes flow and turbulence in an open channel. Experiments are conducted in a 1.5 m wide flume with a 0.25 m deep, 7.29 m long straight pool, entry and exit slopes of 5°, vertical side walls, and gravel sediment (D50 = 9.9 mm). Three‐dimensional velocity components are recorded at 50 Hz using Nortek Vectrinos. Velocity and Reynolds stress profiles in the channel centerline agree with previous results in nonuniform flow and include increased Reynolds stress during deceleration and high velocity near the bed during acceleration. Lateral flow convergence occurs where depth is increasing, which demonstrates that convergence is induced during flow deceleration and does not require a lateral flow constriction. Turbulence during deceleration is characterized by sweeps angled toward the sidewall of the channel, an effect that could lead to the formation of a nonuniform pool depth through lateral gradients in the deposition of mobile sediment. A conceptual model of pool hydrodynamics is proposed that includes increased turbulence, near‐bed acceleration, and lateral flow convergence as linked aspects of convective deceleration and acceleration due to depth changes in the pool.
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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".