The Effect of Grain Size on the Viscosity and Yield Stress of Fine-Grained Sediments
Bibliographic record
Abstract
In debris flow modelling,the viscosity and yield stress of fine-grained sediments should be determined in order to better characterize sediment flow.In particular,it is important to understand the effect of grain size on the rheology of fine-grained sediments associated with yielding.When looking at the relationship between shear stress and shear rate before yielding,a high-viscosity zone（called pseudoNewtonian viscosity） towards the apparent yield stress exists.After yielding,plastic viscosity（called Bingham viscosity） governs the flow.To examine the effect of grain size on the rheological characteristics of fine-grained sediments,clay-rich materials（from the Adriatic Sea,Italy; Cambridge Fjord,Canada; and the Mediterranean Sea,Spain）,silt-rich debris flow materials（from La Valette,France） and silt-rich materials（iron tailings from Canada） were compared.Rheological characteristics were examined using a modified Bingham model.The materials examined,including the Canadian inorganic and sensitive clays,exhibit typical shear thinning behavior and strong thixotropy.In the relationships between the liquidity index and rheological values（viscosity and apparent yield stress）,the effect of grain size on viscosity and yield stress is significant at a given liquidity index.The viscosity and yield stress of debris flow materials are higher than those of low-activity clays at the same liquid state.However the viscosity and yield stress of the tailings,which are mainly composed of silt-sized particles,are slightly lower than those of low-activity clays.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".