On How Episodic Sediment Supply Influences the Evolution of Channel Morphology, Bedload Transport and Channel Stability in an Experimental Step‐Pool Channel
Bibliographic record
Abstract
Abstract We present results from flume experiments in which an 8% steep channel with longitudinal width variations and step‐pool morphology was subjected to sediment feed pulses of different magnitude and frequency under constant water discharge. The channel response to these pulses included (a) large bedload transport rates, (b) bed aggradation, (c) fining of the bed surface, and (d) continuous formation and collapse of steps. In between pulses, the bed surface coarsened, and bedload transport rates dropped by several orders of magnitude. Steps continuously formed and collapsed during and shortly after the pulses, but their stability increased when the sediment feed was turned off. High pulse magnitude enhanced step formation, while low pulse frequency (i.e., long interpulse period) enhanced step stability. We back‐calculated the threshold for motion based on measured bedload transport rates and bed shear stress. Changes in the threshold for motion were much larger than changes in bed surface slope. By accounting for energy dissipation through the effective slope based on flow resistance partitioning, a better prediction was obtained. The threshold for motion decreased following sediment pulses then increased immediately after and fluctuated until the next sediment pulse. Our results indicate that longitudinal width variations and episodic sediment supply are primary controls on the evolution of step‐pool channels. Sediment feed magnitude affects mostly morphological changes, while sediment feed frequency controls channel stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".