The roles of geometry and viscosity in the mobilization of coarse sediment by finer sediment
Bibliographic record
Abstract
In rivers, the addition of finer sediment to a coarser riverbed is known to increase the mobility of the coarser fraction. Two mechanisms have been suggested for this: a geometric mechanism whereby smaller sizes smooth the bed, increasing near-bed velocity and thus mobility of the larger sizes, and a viscous mechanism whereby a transitionally smooth turbulent boundary layer forms, rendering the coarser grains more mobile. Here, we report on experiments using two sediment mixtures to better understand these proposed mechanisms. In Mixture 1, we used 0.5 and 5 mm grains, and in Mixture 2, we used 2 and 20 mm grains. If the entrainment of coarse gravel by finer sediment is a purely geometric effect, then the addition of finer material should produce the same effect on the mobility of the coarser material for both mixtures because they have the same size ratio. We show that addition of finer material has a different effect on the two mixtures. We observed an increase in the mobility of the coarse fraction for both mixtures, but the increase in coarse fraction mobility for Mixture 1 was almost twice that for Mixture 2. Our experiments show that in addition to the geometric effect, enhancement of coarse gravel transport by finer sediment is also driven by a viscous effect.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".