Large-scale flume modelling of segregation processes in debris flows
Bibliographic record
Abstract
Debris flows are powerful natural hazards posing risk to life, infrastructure, and property. Understanding the particle scale interactions in these flows is a key component in the development of models to predict the mobility, distal reach, and hazard posed by a given event. In this study we focus on the process of segregation in debris flows, using a large-scale landslide flume to explore segregation in mixtures of 25 mm, 12 mm, 6 mm, and 3 mm diameter particle sizes. Sample volumes, consisting of a multicomponent mixture of materials, up to 1 m3 in size are released at the top of a 6.8 m long, 2.1 m wide slope, inclined at 30 degrees to the horizontal to initiate flow. Subsequent analysis is completed to determine the extent of vertical and longitudinal segregation of the post-landslide deposit morphology. A range of experimental strategies are explored to provide quantitative measures of particle segregation. Particle size is identified via image analysis and various techniques are applied for the longitudinal sectioning of the deposit, using measurements of segregation at the sidewall of the transparent flume, contrasted with planes measured from within the centre of the deposit. Further, replicate experiments are shown to quantify the probabilistic variation in segregation for multicomponent mixtures of dry granular flows, as well as initially saturated granular flows, to explore the effect of pore fluid on segregation processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".