Process feedbacks that control transport capacity at formative flows in laterally-constrained gravel-bed rivers: a laboratory study
Bibliographic record
Abstract
In gravel-bed rivers, deterministic approaches to predicting bedload transport use the mean bed shear stress (termed one-dimensional or ‘1D’ equations) or integrate across the frequency distribution of shear stress (2D equations). At low flows, incorporating a range of shear stress values increases prediction accuracy, but at relatively high flows the 1D and 2D approaches are similarly accurate. We contribute to an understanding of the stage-dependent relationship between morphology and bedload transport, and specifically why the mean shear stress characterises transport capacity at formative discharges. We performed physical modelling using a generic Froude-scaled model of a steep laterally-constrained gravel-bed river and captured digital elevation models to perform 2D hydraulic modelling. Both 1D and 2D Meyer-Peter Müller equations were highly accurate across two distinct channel morphologies. In alternate bar channels, transport capacity was controlled by negative feedbacks between flow depth and local bed slope that resulted in a relatively homogeneous distribution of bed shear stress. In plane-bed channels, which lacked the degrees-of-freedom available for large-scale morphologic adjustment, transport capacity was controlled by a spatially variable migrating surface texture. The contrasting spatial patterns of morphology, hydraulics, and surface texture between the two channel morphologies highlight the potential for the same correlation between mean shear stress and transport capacity to emerge through different mechanisms. We suggest that nonlinear feedbacks explain why simple bedload transport equations can be highly effective above a certain flow stage across a range of channel morphologies, and further work should examine whether lateral adjustment confounds this result.
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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 | 0.001 |
| 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".