How grain size ratio and fine sediment feed concentration influence channel slope evolution due to grain size sorting in bimodal mixtures
Bibliographic record
Abstract
Grain size sorting in bed material has distinct implications for sediment transport in gravel-bed rivers. As a consequence, the behavior of mixtures differs from that of uniform material. It is essential that understanding of grain size sorting, and its influence upon sediment transport is deepened due to implications for channel stability, ecology and stratigraphy. \nPrevious work has shown how the addition of finer material to a coarse channel bed can enhance the mobility of the coarser sediment due to a reduced entrainment threshold. This change in mobility has been indexed using the change in equilibrium slope within the channel. However, it is not yet known how variations in the grain size ratio (diameter of coarse/diameter of fine), along with the concentration of fine material, influences this behavior. \nNew experimental research has been undertaken which, firstly identifies that degradation can occur when fine sediment is added to a coarse bed, and then shows the grain size ratios and fine sediment feed concentration at which this arises. Additionally the amount of degradation under varying conditions is quantified using the change in equilibrium bed slope. Futhermore, this research also shows that under certain conditions, aggradation can also occur due to the addition of finer sediment to a coarse channel bed. This aggradation, which occurs under given grain size ratios and fine sediment concentrations, is also quantified using the change in equilibrium bed slope.\nThis experimental work was undertaken using bimodal mixtures of spherical glass particles in a relatively narrow sediment-feed-flume. This experimental arrangement allows the control of input conditions, and permits observation of the individual and bulk particle motion.
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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 | 0.001 | 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".