Experiments on the Sediment Transport Along Pool‐Riffle Unit
Bibliographic record
Abstract
Abstract This study examines channel dynamics and bed load transport through a riffle and pool sequence forced by downstream channel width variations within an experimental flume. The experiment consisted of four runs across, which we compare and contrast local and spatially averaged bed surface texture and topography, sediment transport rates, and sediment mobility at five locations across a pool‐riffle pair. Sediment transport was measured using mini Helley‐Smith samplers and particle tracers seeded in the monitored riffle and pool. In this study, “local” sediment transport rates were highly variable across the five monitoring locations. The lowest sediment transport rate was recorded at the riffle tail whereas the highest rates were measured at the riffle head and the pool center. The texture of the bed surface and transported load do not explain measured bed load transport trends and depending on how the measurements are aggregated differing interpretations are supported. In general, the bed texture in the pool was finer than the texture in the riffle, however, specific grain‐size percentile classes derived from pooled population analysis suggests little to no difference between pool and riffle texture. The combined results highlight the importance of acknowledging and applying analysis techniques to better understand the inherent variability of bed load transport within channel reaches where morphology differs, such as pools and riffles.
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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 | 0.001 | 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".