Lateral Erosion of Bedrock Channel Banks by Bedload and Suspended Load
Bibliographic record
Abstract
Abstract Bedrock rivers carry large amounts of fine sediment in suspension. We developed a mechanistic model for erosion of bedrock channel banks by impacting bedload and suspended load particles that are advected laterally by turbulent eddies (advection‐abrasion model). The model predicts high lateral erosion rates near the bed, with rates decreasing up to the water surface. The model also predicts greater erosion within the suspended load layer than the bedload layer for many typical sediment supply and transport conditions explored. We compared the advection‐abrasion model with a previously derived model for lateral erosion of bedrock banks by bedload particles deflected by stationary bed alluvium (deflection‐abrasion model). Erosion rates predicted by the deflection‐abrasion model are lower, except within limited conditions where sediment is transported near the threshold of motion and the bed is near fully covered in sediment. Both processes occur in bedrock rivers at the same time, so we combined the advection‐abrasion and deflection‐abrasion models and found that the lateral erosion rate generally increases with increasing transport stage and relative sediment supply for a given grain size. Application of our combined‐abrasion model to a natural bedrock river with a wide distribution of discharge and supply events, and mixed grain sizes, indicates that finer sediment dominates the lateral erosion on channel banks in low sediment supply environments and can be as important as coarser sediment in high sediment supply environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".