The Effect of Episodic Sediment Supply on Bedload Variability and Sediment Mobility
Bibliographic record
Abstract
Abstract The effect of sediment supply on sediment mobility is analyzed for a poorly sorted (0.5–64 mm) experimental bed. Water discharge was held constant over a sequence of seven runs, and 300 kg of sediment was supplied during each run in different magnitudes and frequencies. In runs with constant feed bedload transport rate increased gradually. In contrast, runs that received large sediment pulses showed pronounced increases in bedload rate as the bed surface got finer, followed by monotonic declines as the bed surface coarsened. We studied the temporal scales of bedload fluctuations by means of sample autocorrelation coefficients and the rates of decrease in bedload fluctuation with sampling time scale. The significant trends caused in bedload rate by large occasional sediment pulses increased long‐term autocorrelation in bedload rate time series relative to runs with constant feed. Bed evolution and local changes in sediment storage caused multiple scales of variability in bedload rate, which increased autocorrelation and caused long‐term persistence in bedload series over periods with a nearly constant mean. The scaling statistics of bedload transport fluctuation depended on grain size, and those for total bedload were similar to those for fine gravel (2–8 mm), which was fully mobile and dominated bedload transport. Grain size dependence of bedload fluctuation was not affected by changes in sediment feed because water discharge and sediment texture were held constant.
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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) | 0.001 | 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".