Why do large, deep rivers have low-angle dune beds?
Bibliographic record
Abstract
Abstract Dunes are the most common bedform in sand-bedded rivers. Small, high-angle dunes (HADs) dominate in shallow (<2.5 m) flows and have lee sides with steep downstream-facing slipfaces (>24°) and reversed upslope-oriented flow in the separation vortex. In contrast, large, deep (>2.5 m) rivers have low-angle dunes (LADs) with gentle lee-side slipfaces (<24°; often <10°), little or no flow separation, and more-pronounced downslope currents. Avalanches driven primarily by particle interaction maintain slipfaces of HADs. On LADs, excess pore pressures that occur during the failure of thick, loosely packed deposits can produce liquefied avalanches that flow and stop on gentle gradients. As lee-side angles decrease over LADs, downslope currents increase in strength, accelerating avalanches, transporting bedload, and creating smaller, superimposed dunes and uniform concave slopes on the lee side. Nearly a century of research on dunes in shallow laboratory flows has assumed that dune morphodynamics are scale invariant, which is not true.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".