Grain Size‐Specific Engelund‐Hansen Type Relation for Bed Material Load in Sand‐Bed Rivers, With Application to the Mississippi River
Bibliographic record
Abstract
Abstract Many sand‐bed rivers worldwide have been experiencing significant reductions in sediment load over the past several decades. This is one of the causes of river delta drowning worldwide. This problem, however, has not been studied in detail in the context of sediment grain sorting. Considering the good performance of the original Engelund‐Hansen relation (OEH) for uniform sediment, and the fact that bulk grain size‐specific relations for bed material load that allow for sorting are relatively rare in the case of sand‐bed rivers, a grain size‐specific Engelund‐Hansen type relation (SEH) is proposed in this study based on data from a large flume. We embed both the OEH and the SEH in a one‐dimensional river morphodynamic model to simulate the morphodynamic evolution of the middle Mississippi River in response to the upstream cutoff of sediment supply. Simulation results using a single characteristic grain size show that bed material load delivered to the delta reduces only gradually in response to the cutoff of sediment supply, in agreement with previous studies. However, implementing the full grain size distribution of sediment leads to a much faster reduction of bed material load delivered to the delta, because the bed surface coarsens in response to grain sorting. This armoring inhibits the bed degradation that would replenish sediment load along the channel. The bed material load of finer sediment declines more rapidly than that of coarser sediment. The results of this study have practical implications for river delta restoration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".