A new method to predict average flow velocity and conveyance capacity of meandering streams
Bibliographic record
Abstract
This paper aims to be a contribution to the evaluation of the resistance factor of the alluvial meandering streams of natural origin. The well-known “divided resistance” approach, commonly adopted nowadays for straight streams with a bed covered by ripples and dunes is extended to include an additional term that considers the resistance due to the meandering of the stream. For the present analysis, 40 laboratory experiments and 285 field observations are evaluated, where it is found that the contribution of the meandering of the stream to its overall resistance may vary from negligible to very substantial, which depends on the stream geometric and flow conditions. In addition, it is determined that the most influential components for the resistance factor in meandering conditions depend on the relation between channel-averaged flow depth (hav) and the average grain size of the bed material (D50), the deflection angle of a meandering flow at the crossover Oi (θ0), and also the relation between flow width (B) and the channel-averaged flow depth (hav); these relations are used to develop a mathematical expression capable to predict the resistance to flow due to the meandering of the stream. It is shown that the equation introduced in this paper leads to considerably improved predictions of average flow velocity and conveyance capacity of meandering streams, which is of significance for an improved management of inland waterways.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".