Influence of pore fluid on grain-scale interactions and mobility of granular flows of differing volume
Bibliographic record
Abstract
The presence of a pore fluid is recognized to significantly increase the mobility of saturated over dry granular flows. However, experimental studies in which both the bulk-scale (runout) and grain-scale behaviour of identical granular material in a dry and saturated initial state are directly compared are rare. Further, the mechanisms through which pore fluid increases mobility may not be captured in experimental flows of small volume typical of laboratory conditions. Here we present the results of dry and initially fluid saturated or ‘wet’ experimental flows in a large laboratory flume for five source volumes of 0.2 to cubic metre. Our results demonstrate that the striking differences in the nature of interactions at the particle scale between wet and dry flows can be directly linked to macro-scale behaviour: in particular, a greatly increased mobility for wet granular flows compared to dry, and a significant influence of scale as controlled by source volume. This dataset provides valuable test scenarios to explore the fundamental mechanisms through which the presence of a pore fluid increases flow mobility by first constraining the frictional properties of the material (dry experiments), permitting an independent evaluation of the implementation of interstitial fluid effects in numerical runout models (wet experiments).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".