The effect of shear-dependent flocculation on the multimodality of effective particle size distributions in a gravel-bed river during high flows
Bibliographic record
Abstract
Purpose: Multimodal effective particle size distributions (EPSDs) develop as flocculation and particle breakage occur dynamically in a fluid shear and such distributions have been previously reported in coastal and estuarine waters to understand flocculation processes. Here, we use time varying multimodal EPSDs and hydraulic parameters (discharge and bed shear stress) to assess freshwater flocculation in a gravel-bed river in southern Alberta, Canada. Methods: Instantaneous discharge, volume concentration (VC), and EPSD of suspended solids were measured during three high discharge events at four study sites in a 10 km reach of the Crowsnest River. The EPSD and VC of suspended solids (< 500 µm) were measured in the centroid of flow with a LISST-200x. Bed shear stress for measured discharge was obtained using a flow model, MOBED. Results: Multimodal EPSDs consisted of primary particles, flocculi, microflocs, and macroflocs. Shear dependent flocculation was consistently observed for all sites and events, due to low and high shear stress flocculation, particle breakage, and mobilization of tributary sub-catchment derived particles. Higher shear stress limited flocculation to smaller floc sizes, while lower bed shear stress conditions created higher volumes of macroflocs. Conclusion: Flocculation and particle breakage processes based on relationships between particle size and hydraulic properties presented herein have implications for advancing fine sediment transport models by a variable cohesion factor as a function of floc size class.
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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".