A rheological and microstructural study of two-step yielding in mud samples from a port area
Bibliographic record
Abstract
Natural fine-grained suspensions usually exhibit a complex rheological fingerprint – in particular a two-step yielding phenomenon – due to the presence of mineral clay particles and organic matter (often found in a flocculated state). These rheological properties may vary considerably from one location to another due to the differences in mud composition (specifically in organic matter content). In this study, the origin of this two-step yielding behaviour for natural suspensions is discussed with the help of different experimental techniques including rheology, particle sizing, rheo-optics, and video microscopy. The samples were collected from different locations in the Port of Hamburg, Germany. A rheological analysis of the samples was performed with amplitude sweep, frequency sweep, stress ramp-up and structural recovery tests. The shear-induced structural changes of mud samples was studied by using a parallel plate shearing device with a microscope. Mineral clay-organic matter flocs were studied using video microscopy to obtain the floc size, floc density and settling velocity of flocs. Higher values of rheological properties such as cross-over stress, yield stress, and moduli were observed for samples having higher organic matter content. These samples also produced the largest floc sizes. The rheo-optical analysis showed the formation of cylinder-like structures in fine-grained suspensions upon shearing action, which reflect the origin of two-step yielding behaviour in mud samples, observed in stress ramp-up and amplitude sweep tests.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".