Sediment pulses in mountain rivers: 1. Experiments
Bibliographic record
Abstract
Sediment often enters rivers in discrete pulses associated with landslides and debris flows. This is particularly so in the case of mountain streams. The topographic disturbance created on the bed of a stream by a single pulse must be gradually eliminated if the river is to maintain its morphological integrity. Two mechanisms for elimination have been identified: translation and dispersion. According to the first of these, the topographic high translates downstream. According to the second of these, it gradually diffuses away. In any given river both mechanisms may operate. This paper is devoted to a description of three controlled experiments on sediment pulses designed to model conditions in mountain streams. Each of the experiments began from the same mobile‐bed equilibrium with a set rate and grain size distribution of sediment feed. In one experiment the median size of the pulse material was nearly identical to that of the feed sediment. In the other two the pulse material differed in grain size distribution from the feed sediment, being coarser in one case and finer in the other case. In all cases the mode of pulse deformation was found to be predominantly dispersive, a result that constitutes the main conclusion of this paper. The pulses resulted in a notable but transient elevation of sediment transport rate immediately downstream. When the pulse was coarser than the ambient sediment, the bed downstream remained armored, and a migrating delta formed in the backwater upstream. When the pulse was finer than the ambient sediment, translation was observed in addition to dispersion. The presence of the finer material notably elevated the transport of ambient coarse material on the bed downstream. In part 2 [ Cui et al., 2003 ], the experimental results are used to test a numerical model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".