Complementary experiments for hydraulic modeling of multi-box culverts
Bibliographic record
Abstract
Culvert design must account for a large range of flow rates but most of the time culverts convey flows that are smaller than the design flow. Repeated high flows passing through the culverts lead to a gradual increase of the sediment deposits in the culvert vicinity, eventually resulting in reduced flow and sediment capacity. Sedimentation at culverts is quite an involved process due to the combined effect of flow non-uniformity and unsteadiness, as well as of the flow–sediment interaction. This paper reports results from a series of laboratory experiments aimed at understanding the hydro and morpho-dynamic changes occurring in the vicinity of a three-box culvert. For this purpose, velocity and turbulence characteristics upstream of the culvert were mapped globally and locally during the transition from an original flatbed to equilibrium sediment transport condition using contemporary acoustics, light detection and ranging, and image-based technologies. The experimental results reveal flow complexities not currently accounted for in the design of the multi-barrel culverts, but which can be detrimental for their long-term operation. Considerations on practical approaches to efficiently model the complexities were formulated.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".