Translation and dispersion of sediment pulses in flume experiments simulating gravel augmentation below dams
Bibliographic record
Abstract
The effectiveness of gravel augmentation as a river restoration strategy depends on the extent and duration of the topographic and bed texture changes created by the pulse of added sediment. Previous work has emphasized the strong tendency for natural sediment waves to propagate primarily by dispersion; however, pulse translation may occur for gravel additions to armored channels downstream of dams where added sediments are finer than the preexisting bed material. Here we report results of a laboratory investigation in which we created an immobile, armored bed and documented the spatial and temporal evolution of the bed topography and bed texture in response to gravel pulses of various volumes and grain sizes. The introduced sediment waves evolved by a combination of translation and dispersion, with a significant translational component. Pulse translation and dispersion can be readily discerned on a graph of the time evolution of the downstream cumulative distribution of elevation differences from the preexisting bed topography. Translation was most evident for smaller volumes of added sediment. Pulses of finer‐grained gravel moved through the flume more rapidly, resulting in a larger magnitude but shorter duration of bed fining. More work is needed to understand the influence of bar‐pool topography and flow magnitude and duration before the grain size and volume of gravel additions can be selected to achieve optimal patterns of pulse propagation.
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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 | 0.001 |
| 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".