The Influence of Slipface Angle on Fluvial Dune Growth
Bibliographic record
Abstract
Abstract Dunes dominate the bed of sandy rivers and they respond to flow by changing shape and size, modifying flow, and sediment transport dynamics of rivers. Our understanding of and ability to predict dune adaptation, particularly dune growth and decay, remain incomplete. Here, we investigate dune growth from an initial flatbed in a laboratory setting by continuously mapping the 3D bed topography using a line laser scanner combined with a 3D camera. High‐resolution profiles of flow velocity and sediment concentration providing both bedload and suspended sediment fluxes were obtained by deploying Acoustic Concentration and Velocity Profiler technology. Our analysis reveals that the magnitude of the dune slipface angle, which determines flow separation and controls turbulence production, adjusts to the imposed flow at time scales similar to the evolution of dune height and length. The initiation of a flow separation zone intensifies through scour, and results in acceleration of the dune growth. Gradients in sediment transport and the rate of dune growth are inherently linked to spatial variations in slipface angles. During dune growth, the slipface angle evolves differently than the ratio of dune height to length, which immediately reaches its equilibrium value after dune initiation.
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) | 0.001 | 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".