Influence of Sediment Supply Timing on Bedload Transport and Bed Surface Texture During a Single Experimental Hydrograph in Gravel Bed Rivers
Bibliographic record
Abstract
Abstract Channel stability and sediment transport in gravel bed streams depend on temporally and spatially variable fluid forces, bed surface structures, armoring, and sediment supply/storage. Of particular interest here is the influence of sediment supply timing on bedload transport rate and grain size distribution, bed surface composition and channel morphology. We conducted flume experiments in a sediment feed flume with poorly sorted sediment. A symmetrical, identical stepped hydrograph was used with five different sediment feeding schemes: no feed, constant feed, rising‐limb only feed, falling‐limb only feed, and variable feed. The same sediment mass of 800 kg was fed during each experiment. Sediment transport rates ranged over five orders of magnitude regardless of feeding scheme. Clockwise hysteresis was observed for bedload transport rate and bedload grain size, that is, the transport rate was larger and coarser during the rising limb. Counterclockwise hysteresis was observed for the grain size distribution of the bed surface, that is, the bed surface was finest during the rising limb. In all experiments sediment yield during the rising was higher than during the falling limb, indicating that the rising limb is more capable to transport the supplied sediment. Our study provides insight on how timing of sediment supply influences sediment transport and bed surface during a single hydrograph, essential information for artificial sediment supply projects to restore and habilitate gravel bed streams.
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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 | 0.000 |
| 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".