Erosion and mobility in granular collapse over sloping beds
Bibliographic record
Abstract
We describe laboratory experiments of granular material flowing over an inclined plane covered by an erodible bed, designed to mimic erosion processes of natural flows travelling over deposits built up by earlier events. Two controlling parameters are the inclination of the plane and the thickness of the erodible layer. We show that erosion processes can increase the flow mobility (i.e., runout) over slopes with inclination close to the repose angle of the grains θ r by up to 40%, even for very thin erodible beds. Erosion efficiency is shown to strongly depend on the slope of the topography. Entrainment begins to affect the flow at inclination angles exceeding a critical angle θ c ≃ θ r /2. Runout distance increases almost linearly as a function of the thickness of the erodible bed, suggesting that erosion is mainly supply‐dependent. Two regimes are observed during granular collapse: a first spreading phase with high velocity followed by a slow thin flow, provided either the slope or the thickness of the erodible bed is high enough. Surprisingly, erosion affects the flow mostly during the deceleration phase and the slow regime. The avalanche excavates the erodible layer immediately at the flow front. Waves are observed behind the front that help to remove grains from the erodible bed. Steep frontal surges are seen at high inclination angles over both rigid or erodible bed. Finally, simple scaling laws are proposed making it possible to obtain a first estimate of the deposit and emplacement time of a granular collapse over a rigid or erodible inclined bed.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".