Bibliographic record
Abstract
We develop a multilayer model to study roll waves in mudflow of Herschel–Bulkley fluids initiated by periodic and localized disturbance. Simulations are conducted of the temporal development of periodic roll waves and spatial development of wave packets due to localized disturbance. The results of the temporal development are expressed in terms of the power-law index, the relative plug-layer thickness, the Froude number, and the perturbation wavelength. Our simulation for the spatial development shows the roll waves led by a dominant front runner and followed by a quiescent tail, closely reproducing a well-known river-clogging phenomenon of the natural mudflow observed in the mountain rivers on mild slopes. The leading wave of the roll-wave packet, i.e., the front runner, grows in depth, velocity, celerity, and wavelength with distance from the localized disturbance. The front-runner wave amplitude depends on the distance from the localized disturbance, the power law index, the plug-layer thickness, and the Froude number. We calculated the front-runner’s wave amplitude due to a line source of disturbance in a 1D unidirectional development and the roll waves’ 2D development due to a point source. The initial nonlinear growth in the 2D front runner is a fraction of the 1D waves, but the increase in the wave amplitude with distance follows the same trend. We have also conducted a mesh refinement study to determine the convergence and accuracy. The present simulations using 64 layers have attained an accuracy within a 2% error.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".