Co‐evolution of coarse grain structuring and bed roughness in response to episodic sediment supply in an experimental aggrading channel
Bibliographic record
Abstract
ABSTRACT We use flume experiments to better understand how gravel‐bed channels maintain bed surface stability in response to pulses of sediment supply. Bed elevations and surface imagery at high spatial resolutions were used to quantify the co‐evolution of surface grain‐size distribution (GSD), bed roughness statistics, and bed surface structures (clusters, cells and transverse features). Using a new semi‐automated method, we identified individual stone structures over a 2 m × 1 m area throughout the experiments. After an initial coarsening, surface GSD and armouring ratio remained nearly stable as sediment pulses caused net bed aggradation. In contrast, individual grain structures continued to form, increase or decrease in size, and disappear throughout the experiments. The response of the bed to sediment pulses depended on the history of surface roughness evolution and bed surface structure development, as these factors changed much more in response to supply perturbations earlier in the experiments compared to later, even as the bed continued to aggrade. We interpret that the dynamic production and destruction of bed surface structures can act as a ‘buffer’ to sediment supply pulses, maintaining a stable bed surface during aggradation with minimal change in grain size or armouring. © 2019 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".