Effect of sediment pulse grain size on sediment transport rates and bed mobility in gravel bed rivers
Bibliographic record
Abstract
Sediment supply to gravel bed river channels often takes the form of episodic sediment pulses, and there is considerable interest in introducing sediment pulses in stream restorations to alter bed surface grain size distributions and bed mobility. A series of laboratory experiments was conducted in order to examine how sediment pulse grain size and volume affects the mobility of bed material in gravel bed channels. Pulses used in the experiments were composed of either the fine tail or the median of the subsurface bed material grain size distribution. Bed material refers to sediment in the channel prior to the pulse introduction exclusively. Both types of pulse were finer than the bed material surface median. Two pulse sizes were used, which were either equivalent to the volume of sediment required to cover the entire bed one median subsurface bed material grain diameter deep (full unit) or 1/4 of this volume (1/4 unit). The latter was designed to produce a transitory pulse. With the exception of the 1/4 unit coarse pulse, introduction of the sediment pulses to the channel caused dramatic increases in the bed load flux. The coarse sediment pulses fine the bed surface and coarsen the bed load. Finer pulses also fine the surface, but the bed load fines while the bed material load (that excludes pulse material) coarsens. The greatest effects on the fractional transport occurred during the full unit fine pulse where the pulse covered the greatest bed surface and effectively smoothed the bed, increasing near bed velocity and mobilizing the coarse particles. Overall, the coarse pulses were not very effective at mobilizing bed material. The large fine pulse mobilized ∼35% of the bed material surface (∼35% of its input weight) and was most effective at mobilizing the surface. However, the small fine pulse mobilized 50% of its input weight as it passed through the channel, making it the most efficient at mobilizing the bed material.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 teacher head, 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".