Hydrodynamics of a forced riffle pool in a gravel bed river: 1. Mean velocity and turbulence intensity
Bibliographic record
Abstract
Riffle pools are fundamental units of many gravel bed rivers. Considerable debate exists as to an appropriate hydrodynamic model for this bed form type. Sampling designs in previous studies have not always anticipated the degree of spatial variability of flow parameters in riffle pools nor considered the effect of the nonuniform boundary on hydrodynamics. The objective of this paper is to detail the distribution of mean velocities and turbulence intensities in a forced riffle pool. While limited by the lack of lateral velocity measurements, the data set is of high quality and includes measurements during a bankfull flood. Key observations include the perturbation of velocity profiles from what is observed in uniform flow, lateral convergence and divergence of flow, and the generation of turbulence away from the channel boundary. In the thalweg of the pool head, velocities are high near the water surface and low near the bed. Turbulence intensities are relatively high near the bed and may be significant for sediment entrainment. Higher mean velocities occur over the side bar and could indicate that sediment is routed around the thalweg. The midpool is characterized by the lateral constriction of flow and a shear zone downstream of the forcing element. The pool tail has very high velocities and turbulence intensities near the bed. A velocity reversal occurs in the pool tail at bankfull flow but does not occur in the pool head. A consideration of deceleration and acceleration as a result of vertical expansion and contraction is shown to explain many of the key observations.
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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".