Variation of Flow Pattern with Sinuosity in Sine-Generated Meandering Streams
Bibliographic record
Abstract
The effect of channel sinuosity on flow pattern in meandering streams is investigated. The centerlines of the idealized meandering streams under consideration follow sine-generated curves, and the banks are rigid; the flow is turbulent and subcritical. This study focuses on the vertically averaged flow over a flat (horizontal at any cross section) bed formed by a granular material. The “flat bed” is viewed as the initial surface of a moveable bed at the beginning of an experiment (at time t=0 ). A series of laboratory flow measurements involving the systematic variation of the deflection angle θ0 from 30 to 110° , is used. It is found that every different sinuosity (every different θ0 ) has its own convective flow pattern, i.e., its own distribution in plan of (the L∕2 long) convergence–divergence zones of flow. As θ0 increases, a gradual change in flow pattern is observed. Two expressions defining the observed θ0 variation of the convective flow pattern are introduced. It is shown, with the aid of the sediment transport continuity equation, that the geometry of the developed bed at the end of an experiment is strongly related to the convective behavior of the vertically averaged (initial) flow over the flat bed at t=0 . In particular, information on the θ0 variation of the convective pattern of the initial flow can be used to estimate the location of erosion–deposition zones and the location(s) of the most intense erosion–deposition corresponding to any θ0 .
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".