Bibliographic record
Abstract
As the construction of infrastructure in and around a river channel modifies the flow and sediment transport characteristics, prediction of scour depths is an integral part of engineering design. Whilst there are a substantial number of predictive equations available to estimate the scour resulting from bridge piers, other types of structures have been less examined. This paper reports on flume experiments and numerical modelling to examine the role of submerged sills in the development of scour. Sills can be utilised in a channel to control bed stability, influence mixing dynamics or dictate water levels. Additionally, sills can occur due to infrastructure including riprap-covered pipeline crossings. The majority of previous work on sills has been undertaken on unsubmerged or partially submerged sills. The only experimental work on submerged sills is restricted to a range of conditions not applicable to pipeline crossings in large sand-bed rivers. This paper presents experiments, using artificial light-weight sediment (crushed walnut shell), undertaken with a fixed flow depth but a range of velocities and sill heights, all of which are in the surface flow regime. Hydrodynamic numerical simulations were run for the same conditions to gain insight into the corresponding flow structure.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".