Channel evolution after dam removal in a poorly sorted sediment mixture: Experiments and numerical model
Bibliographic record
Abstract
Abstract Dam removal is commonly used for river restoration. However, there are still some uncertainties associated with dam removal, mainly related to the sediment transport rates released downstream from the deposit that had previously filled the impoundment. This research studies the physical response to dam removal in the antecedent deposit by answering the following questions: (a) how does an initial channel excavated into the deposit evolve, and (b) what is the time distribution of the material released during the early stages of the process. These goals are achieved by an experimental campaign using a poorly sorted mixture of sediment in the antecedent deposit. The research shows that for the given conditions of our experiments, the rate at which the sediment is released depends on the height of the removed dam, the water discharge and the maximum potential volume of sediment to be eroded. This investigation provides new insights of the width evolution when the sediment is composed of a poorly sorted mixture. This evolution is linked to the bed degradation rates: channel narrows during a rapid incisional phase, and subsequently widens when bed degradation rates decrease. Channel width changes propagate upstream as a convection‐like perturbation associated with a kinematic wave starting at the location of the antecedent dam. These features are modeled through a new numerical model accounting for mixtures. More specifically, a set of equations has been derived for the variation of bed elevation, channel bottom width, and bed grain‐size distribution, which when solved numerically, describe the observed channel processes.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".