Size, shape and dynamics of large-scale turbulent flow structures in a gravel-bed river
Bibliographic record
Abstract
In this paper, we present a detailed investigation of the size, scale and dynamics of macro-turbulent flow structures in gravel-bed rivers. We used an array of seven electromagnetic current meters with high resolution in both space and time to measure the streamwise velocity fluctuations in a gravel-bed river. The array was deployed successively in various configurations in order to quantify the vertical, lateral and longitudinal extent of the flow structures and to estimate their advecting velocities. To depict the spatial and temporal properties of the flow structures, we used space–time velocity matrices, space–time correlation analysis and coherent-structure detection schemes. The results show that the large-scale turbulent flow structures in a gravel-bed river occupy the entire depth of the flow and that they are elongated and narrow. The length of the structures is 3 to 5 times the flow depth while the width is between 0.5 and 1 times flow depth. In spite of the high roughness of the bed, these values are similar to those reported in the literature for laboratory experiments on large-scale turbulent flow structures. The dynamics of the large-scale turbulent flow structures investigated using flow visualization highlight the interactions between the outer flow region and the near-bed region. Our evidence suggests that large-scale flow incursions trigger ejections in the near-bed region that can develop into megabursts that can reach the water surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".