Experimental Investigation of Erosion Characteristics of Fine-Grained Cohesive Sediments
Bibliographic record
Abstract
In this short communication, the erosion process of the fine, cohesive sediment collected from the upper River Taw in South West England was studied in a rotating annular flume located in the National Water Research Institute in Burlington, Ontario, Canada. This study is part of a research project that is underway to model the transport of fine sediment and the associated nutrients in that river system. The erosion experimental data show that the critical shear stress for erosion of the upper River Taw sediment is about 0.09 Pa and it did not depend on the age of sediment deposit. The eroded sediment was transported in a flocculated form and the agent of flocculation for the upper River Taw sediment may be due to the presence of fibrils from microorganisms and organic material in the system. The experimental data were analysed using a curve fitting approach of Krone and a mathematical model of cohesive sediment transport in rotating circular flumes developed by Krishnappan. The modelled and measured data were in good agreement. An evaluation of the physical significance of Krone’s fitting coefficients is presented. Variability of the fitting coefficients as a function of bed shear stress and age of sediment deposit indicate the key role these two factors play in the erosion process of fluvial cohesive sediment.
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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) | 0.001 | 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".