Multiplicative noise and intermittency in bedload sediment transport
Bibliographic record
Abstract
Sediment transport in rivers near the threshold of grain motion is characterized by rare but large transport events. This intermittency makes it difficult to relate average sediment flux to average flow conditions, or to define an unambiguous threshold for grain entrainment. Although intermittent sediment transport can be observed and characterized, its origins are unclear. In this study we investigate bedload sediment transport near the threshold of grain motion in an experimental flume to examine the origins of intermittency. We apply image-processing techniques to high-speed video of grains in a narrow flume, which allows us to track individual particles and measure statistics of particle motion. Bedload sediment transport near the threshold of grain motion is very low, allowing us to approximate the time evolution of the sediment flux via a polynomial expansion, including a linear growth rate and a nonlinear term which saturates the growth. We introduce a noisy coefficient to the linear growth rate term (“multiplicative noise”), rather than adding the noise to the equation, to model the inherent fluctuations in the system. We demonstrate that multiplicative noise near the threshold of grain motion can account for the observed intermittency. We use analytical results from bifurcation theory in the presence of multiplicative noise to analyze our experimental results, quantifying the noise responsible for the intermittency and calculating the critical shear stress for grain entrainment in a novel way that is consistent with the physics of grain motion at low transport stages.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".