The Effect of Sediment Supply on Pool‐Riffle Morphology
Bibliographic record
Abstract
Abstract Downstream width variations can generate pool‐riffle morphology under experimental conditions, in numerical simulations and natural river channels. The present understanding of how pool‐riffle morphology varies with sediment supply and caliber, however, is insufficient due to the limited range of sediment supply rates explored in previous experiments and the little attention paid to sand supply and sediment size distribution in the laboratory and in the field. We present a model of river morphodynamics that can account for the spatial variability of channel width, and we validate the model with experimental data. Model validation shows how this one‐dimensional model can capture pool‐riffle formation, growth, and equilibration with errors that are comparable with those of other 1D models of river morphodynamics. We then apply the validated model to study the effects of sediment supply rate and caliber on pool‐riffle morphology. Model results show that pool‐riffle morphology is resilient to the range of tested sediment supply (i.e., five‐fold the sediment amount, 41‐fold the sand amount and coarsening the gravel supply). Bed and water surface slopes are sensitive to all types of change of sediment supply, whereas the sensitivity of bed surface sediment grain size varies with the type of change. These findings support prior research emphasizing the role of downstream width variations for the development/maintenance of pool‐riffle morphology and can help in the restoration and recovery of pool‐riffle gravel‐bed rivers.
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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 | 0.001 |
| Research integrity | 0.000 | 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".