Difference in the bed load transport of graded and uniform sediments during floods: An experimental investigation
Bibliographic record
Abstract
Abstract The objective of this study was to experimentally evaluate the difference in the transport of uniform (5.17, 10.35, 14, 20.7 mm) and graded sediment (mixture of these rounded particles with equal weight proportions) under different unsteady flow hydrographs in a 12 m long, 0.5 m wide and deep glass-walled flume. There was a lag time between fractions and uniform particles, such that peaks of coarser and finer fraction particles occurred before and after the peak of uniform sediment with the same size, respectively. Comparison between uniform particles and fractions in graded sediment showed that the sediment transport rate of fine and coarse fractions was lower and higher than their counterpart uniform particles, respectively. Overall, the uniform particles demonstrated a clockwise hysteresis loop and graded sediment had a counterclockwise hysteresis loop. The mobility of coarser fractions increased during the rising limb of hydrograph, whereas the mobility of finer fractions increased during the falling limb. In general, the mobility of coarse fractions increased and that of fine fractions reduced. Result of transported sediment showed that average particle size collected in traps (Db50) was coarser than bed material (Ds50) on both limbs. The relative transport ratio for uniform and graded sediment is higher and lower than 1, respectively.
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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) | 0.001 | 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".