Assessing Experimental Methods for the Quantification of Particle Size Segregation in Large Scale Flume Tests using Image Analysis
Bibliographic record
Abstract
Particle size segregation is a phenomenon that generates preferential sorting of particles, based on size, in material flows of non-uniform size distribution. Landslide hazards, such as debris flows, involve materials of non-uniform particle sizes and therefore generate flow structures which arise from particle size segregation. The mobility, distal reach and impact forces associated with these natural hazards are influenced by these processes. Understanding the mechanisms of this phenomenon is essential for acquiring accurate input parameters that are needed to model these flows and properly design debris flow barriers and retaining structures. While the dynamics of particle size segregation in flow and deposition have been furthered through studying granular flows, studies to date have had several limitations. They primarily examine flows of bidispersed material, are small in scale, and rely on observations from flume sidewalls, precluding the study of dynamics along the centreline of flows. In this study, a large scale 6.8 m long and 2.1 m wide slope inclined at 30 degrees was used to generate dry tridispersed granular flows with 0.6 m3 of material. The tridisperse mixture consisted of even proportions by mass of 3 mm, 6 mm and 12 mm diameter spherical particles. Replicate tests were conducted to observe flow dynamics and assess methods for sampling along the internal plane of the test deposit. Image analysis techniques were developed to quantify particle size distributions within the deposit. Flume sidewall and internal observations were found to differ significantly from each other, in that side wall observations contained significantly higher proportions of the largest particle size. Additional replicate tests were conducted with saturated material to further examine the impact of pore fluid on segregation. This work will allow for future calibration of both numerical and theoretical models of particle size segregation and ultimately enable better debris flow modelling and mitigation practices.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".