Intermittent grain activity from grain-scale collective entrainment rules
Bibliographic record
Abstract
In bed load sediment transport, grains are moved by turbulent flow and are in nearly continuous contact with other grains, resulting in sediment flux that is intermittent, displaying bursts of activity in both space and time. Understanding the dynamical origin of these fluctuations is a challenge. Grain-scale models resolve the grain-fluid coupling and provide insight into the grain-scale sources of fluctuations, but are impractical to apply at the channel scale. On the other hand, landscape evolution models and other continuum treatments of alluvial channels ignore fluctuations by averaging over grain-scale processes. We introduce an intermediate-complexity lattice model, or cellular automaton, of bed load sediment transport with grain dynamics based on a simple set of rules. Mimicking collision interactions, grains in our model are entrained by their mobile neighbors with a probability that depends on the local bed slope. When a grain is entrained from or deposited onto the bed, it changes the bed elevation, providing a feedback between entrainment and bed topography. Despite this simplified representation of grain dynamics, the model reproduces the intermittent statistics of grain activity observed in previous studies. Numerical experiments further reveal how intermittency depends on bed width and point to a physical explanation. Finally, we use our model to derive a stochastic partial differential equation describing the system, providing a direct connection between collective entrainment and a nonlinear source term for grain entrainment, which is responsible for the intermittent dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".