Bibliographic record
Abstract
Erosion characteristics of fine sediment deposits were studied experimentally using a rotating circular flume in the laboratory. The influence of the rate of application of bed shear stress and the structure of sediment beds was investigated. When the shear stress was applied suddenly on a bed formed by placing a thick slurry of kaolin in the flume, the concentration of the eroded sediment first increased to reach a maximum value, and then it started to decrease and finally attained a steady state value that was significantly lower than the maximum concentration (the steady state concentration value was one third of the maximum concentration value). When the shear stress was applied gradually, the behaviour was similar, but the difference between the values of the maximum concentration and steady state concentration was very small (<10%). When the shear stress was applied gradually on a bed formed by depositing a dilute suspension, the concentration increased monotonically and reached the steady state concentration without exhibiting the hump. Such a behaviour was attributed to the sorting of flocs in the settling of dilute suspension in which stronger flocs deposit first followed by weaker flocs in succession.Key words: kaolin, erosion rate, bed shear stress, bed structure, rotating circular flume, flocculation, size distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".