A mechanistic model for lateral erosion of bedrock channel banks by bedload particle impacts
Bibliographic record
Abstract
Bedrock rivers get wider by lateral erosion. Lateral erosion is widely thought to occur when the bed is covered by alluvium, which deflects the downstream transport of bedload particles into channel walls. Here we develop a model for lateral bedrock erosion by bedload particle impacts. The lateral erosion rate is the product of the volume eroded per particle impact and the impact rate on the wall. The volume eroded per particle impact is modelled by tracking the motion of bedload particles from collision with roughness elements to impacts on the wall. The impact rate on the wall is zero if the bedload particle deflected by roughness elements cannot reach the wall. Otherwise, the impact rate on the wall is the same with that on roughness elements. The model further incorporates the co-evolution of wall morphology, shear stress and erosion rate. The model predicts the undercut wall shape observed in physical experiments. The non-dimensional lateral erosion rate is used to explore how lateral erosion varies under different relative sediment supply (ratio of supply to transport capacity) and transport stage conditions. Maximum lateral erosion rates occur at high relative sediment supply rates (~ 0.7) and moderate transport stages (~10). The competition between lateral and vertical erosion is investigated by coupling the saltation-abrasion vertical erosion model with our lateral erosion model. The results suggest that vertical erosion dominates under near 75% of supply and transport stage conditions, but is outpaced by lateral erosion near the threshold for full bed coverage.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".