Evaluating Effective Particle Size Distributions of Cohesive Sediment under Varying Shear Stress and Bed Configurations in a Rotating Annular Flume
Bibliographic record
Abstract
Despite the environmental significance and ecological importance of cohesive sediment (<63 μm), improved knowledge of how effective particle size distributions (EPSDs) change due to flocculation under different conditions of shear stress and bed configuration is required to better understand in situ transport and storage properties and refine existing sediment transport models. Here, a rotating annular flume was used to (i) evaluate EPSDs under different shear stress and bed types (plane-impermeable and -porous gravel bed) for deposition and erosion experiments; (ii) assess flocculation processes with EPSDs; and (iii) compare flume and field EPSDs observations with respect to measured shear stress. While deposition experiments over the impermeable bed led to an EPSD equilibrium in all shear conditions (constant EPSD percentiles), the ingress experiment over the gravel bed resulted in varying EPSDs, and no equilibrium was observed. During the erosion experiment, deposited flocs became coarser due to bed consolidation, and no particle breakage was observed once particles were resuspended. The ingress experiment showed high efficiency in entrapping suspended particles (~95% of initial suspended sediment), and no exfiltration or resuspension was recorded. Flocculation ratios calculated using EPSDs showed negative correlations with shear stress, indicating that increasing flow energy promoted flocculation for flume and field observations. Our results showed that both suspended and bed sediments can flocculate into coarser flocs that, in turn, are preferentially ingressed and stored in the substrate when in suspension. These findings have important implications regarding legacy impacts, as substrate-stored particles can potentially extend the effects of upstream landscape disturbances.
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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 | 0.000 | 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".