Channel adjustments to a succession of water pulses in gravel bed rivers
Bibliographic record
Abstract
Abstract Gravel bed rivers commonly exhibit a coarse surface armor resulting from a complex history of interactions between flow and sediment supply. The evolution of the surface texture under single storm events or under steady flow conditions has been studied by a number of researchers. However, the role of successive floods on the surface texture evolution is still poorly understood. An experimental campaign in an 18 m‐long 1 m‐wide flume has been designed to study these issues. Eight consecutive runs, each one consisting of a low‐flow period of variable duration followed by a sudden flood (water pulse) lasting 1.5 h, have been conducted. The total duration of the experiment was 46 h. The initial bed surface was created during a 280 h‐long experiment focused on the influence of episodic sediment supply on channel adjustments. Our experiments represent a realistic armored and structured beds found in mountain gravel bed rivers. The armor surface texture persists over the duration of the experiment. The experiment exhibits downstream fining of the bed‐surface texture. It was found that sorting processes were affected by the duration of low‐flow between flood pulses. Since bed load transport is influenced by sediment sorting, the evolution of bed load transport is impacted by the frequency of the water pulses: short interpulse durations reduce the time over which fine material (transported as bed load) can be winnowed. This, in turn, contributes to declining reduction of the bed load transport over time while the sediment storage increases.
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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 | 0.001 | 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".