Bridging the gap between turbulence and larger scales of flow motions in rivers
Bibliographic record
Abstract
ABSTRACT Although flow turbulence in rivers is of critical importance to earth scientists, ecologists and engineers, its relations with larger flow scales are not well understood, thus leaving a fundamental gap in our knowledge. From an analysis of a long time series of the streamwise and vertical flow velocity fluctuations measured in a gravel‐bed river, we show that the signature of the fundamental turbulent flow structures (e.g. ejections and sweeps) is embedded within increasingly larger flow scales in a self‐similar manner. The imbrication of turbulent structures into large flow pulsations of flow acceleration and deceleration covers more than two‐orders of magnitude from a few seconds to nearly 10 minutes. This property is explained by the clustering of turbulent events creating an emergent pattern at larger scales. The size of the larger flow pulsations scales with the spacing of the pools and riffles in the river. This implies a mutual adjustment between turbulence generation mechanisms and long pulsations of flow acceleration and deceleration controlled by the bed morphology. These results bridge a gap in our understanding of flows in rivers and offer a new perspective on the interactions between the turbulent flow with larger scales of flow motion that are critical for sediment transport, habitat selection and fish behaviour. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".