Effects of interactions between transient granular flows and macroscopically rough beds and their implications for bulk flow dynamics
Bibliographic record
Abstract
Steep-creek beds are macroscopically rough. This roughness causes channelized flow material to decelerate and dissipate energy, which are accounted for by depth-averaged mobility models (DMM). However, practical DMM implementations do not explicitly account for grain-scale basal interactions that influence macroscopic flow dynamics. In this study, we model flows using physical tests with smooth and macroscopically rough bases, and hence evaluate discrete element method (DEM) and DMM models. A scaling effect is identified relating to roughened beds: increasing the number of grains per unit depth tends to suppress dispersion, such that small-scale flows on smooth beds resemble large-scale flows on roughened beds, at least in terms of bulk density. Furthermore, the DEM shows that rougher beds reduce the peak bulk density by up to 15% compared to a smooth bed. Rough beds increase the vertical momentum transfer tenfold, compared to smooth ones. The DMM cannot account for density change or vertical momentum, so DMM flow depths are underestimated by 90% at the flow front and 20% in the body. The Voellmy model implicitly captures internal energy dissipation for flows on rough beds. The parameter ξ can allow velocity reductions due to rough beds observed in the DEM to be captured.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".