Prediction of scour profiles downstream of grade control structures via the shear stress and sediment bed curvature model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A new semianalytical model for the prediction of local scour profiles downstream from typical grade-control structures is proposed on the basis of the variation in bed shear stress and the sediment bed curvature concept. The proposed method was applied to a continuous boundary between the flow and sediment regions to predict the scour profile downstream of submerged sharp-crested weirs. By applying the momentum equation, the nappe flow over the weir was modeled as an oblique point force on the bed surface boundary, and the eroded profile was represented by a system of differential equations. The scour length and sediment resistance strength are the two unknowns in the shear stress and sediment bed curvature (SSC) differential equations. A series of laboratory experiments were carried out under clear water conditions to evaluate the accuracy and performance of the proposed model. The scour profile was calculated via prediction equations that are based on the known maximum scour depth, d max , which was proposed in this study and in the literature. The effects of the submergence ratio and flow intensity on the maximum scour depth and scour profile were investigated, and a model was developed to predict the equilibrium scour depth. The prediction error associated with equations based on the equilibrium scour depth, d max , resulted in a significant error in scour length prediction. Furthermore, the deviation between the measured and predicted geometrical characteristics was also correlated with predictions of d max and scour length, L , as functions of flow intensity and submergence.
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.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it