Simulated Dynamics of Mixed Versus Uniform Grain Size Sediment Pulses in a Gravel‐Bedded River
Bibliographic record
Abstract
Abstract Mountain rivers often receive sediment in the form of episodic, discrete pulses from a variety of natural and anthropogenic processes. Once emplaced in the river, the movement of this sediment depends on flow, grain size distribution, and channel and network geometry. Here, we simulate downstream bed elevation changes that result from discrete inputs of sediment (∼10,000 m3), differing in volume and grain size distribution, under medium and high flow conditions. We specifically focus on comparing bed responses between mixed and uniform grain size sediment pulses. This work builds on a Lagrangian, bed‐material sediment transport model and applies it to a 27 km reach of the mainstem Nisqually River, Washington, USA. We compare observed bed elevation change and accumulation rates in a downstream lake to simulation results. Then we investigate the magnitude, timing, and persistence of downstream changes due to the introduction of synthetic sediment pulses by comparing the results against a baseline condition (without pulse). Our findings suggest that bed response is primarily influenced by the sediment‐pulse grain size and distribution. Intermediate mixed‐size pulses (∼50% of the median bed gravel size) are likely to have the largest downstream impact because finer sizes translate quickly and coarser sizes (median bed gravel size and larger) disperse slowly. Furthermore, a mixed‐size pulse, with a smaller median grain size than the bed, increases bed mobility more than a uniform‐size pulse. This work has important implications for river management, as it allows us to better understand fluvial geomorphic responses to variations in sediment supply.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".