Finding of Suitable Transportation Rate Formula by Using "Velocity - Height - Distance" for Bed Material Load
Bibliographic record
Abstract
This research aims to find a suitable transportation rate formula for bed load. Laboratory experiments were conducted in a flume for monitoring and measuring the transportation of sediment particles (bed load) which move by jumping, rolling, or sliding within the flume. This study included two parts. The first part is experimenting using a flume and tracking the particles' movement by analysing the images taken by the camera. The measurements that have been taken are the amount of accumulated bed load particles at the end of the flume, which are distributed along a certain distance during a specific time, thus obtaining measuring data about the amount of accumulated bed load and values of moving distance and the required time for accumulating the particles at the flume end. The accumulated height of bed load is also measured. These experiments were conducted at different low-flow velocities. The second part includes the expression of a formula for bed load transportation rate, which is the product of multiplying the accumulated height of bed load by the velocity of bed load particles with distance at a certain time that was devised in the first part. Through the proposed method of obtaining the measurements in the first part Analysis of bed load particle velocity was done by using (π-theorem) from the results of experiments (Cv, V, ρ, ρS, µ, ds, L). To derive the formula of accumulated bed load height at flume end along a certain distance using Rayleigh’s method and using the results of the experiment (δ, V, g, ds). Finally, there can be found an expression of the bed load transportation rate formula. Checking was made for this formula, which was compared with other researchers' equations using statically measured. It was found that the derived formula was acceptable to calculate the transportation rate of bed load.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".