Transport Scaling of Dune Dimensions in Shallow Flows
Bibliographic record
Abstract
Abstract Dune dimensions in sand‐bedded rivers are often thought to scale with flow depth ( h ), with height ( H ) scaling as 1/6 h and length ( L ) as 5 h , even though substantial scatter about the relations has been observed. Transport stage has been shown to affect bedform geometry, but this control is often ignored in favor of depth scaling relations. Here, we use a series of flume experiments to systematically test controls on dune dimensions and variability. Experiments involved three sets of runs under five constant transport stages, ranging threshold to washout conditions, at three different flow depths. The mobile bed was repeatedly scanned during a 10‐hr equilibrium period to derive mean values and quantify the variability. The results show that dune‐depth scaling is not consistent because of a transport stage effect. Dune height increases with transport stage until a point when H decreases. Length remains nearly constant with transport stage until further increases in transport stage leads to lengthening. In general, dunes grow higher when significant bedload transport occurs but become flatter and longer in the presence of substantial suspension. Ultimately, dunes scale with transport stage, which is a function of slope, grain size, and h . The results are used to derive transport stage relations to guide predictions of dune dimensions in rivers and reconstructions of paleoflows based on dimensions estimated from cross strata. The relations incorporate the nonlinear response of dune dimensions with transport stage and provide metrics of uncertainty to include in predictions.
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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 | 0.000 |
| 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".