Spatial patterns of water surface topography at a river confluence
Bibliographic record
Abstract
Abstract Understanding flow structures in river confluences has largely been the product of interpretations made from measured flow velocity data. Here, we turn the attention to the investigation of the patterns of both the average and standard deviations of the micro‐topography of the water surface at an asymmetrical natural discordant confluence for different flow conditions. Water surface topography is measured using a total station to survey the position of a reflector mounted on a custom‐built raft. To limit error problems related to changes in the water level, measurements are taken and analysed by cross‐stream transects where five water surface profiles are taken before moving to the next transect. Three‐dimensional numerical simulations of the flow dynamics at the field site are used to examine predicted water surface topography for a steady‐state situation. The patterns are interpreted with respect to flow structure dynamics, visual observations of boils, and bed topography. Results indicate that coherent patterns emerge at the water surface of a discordant bed confluence for different flow conditions. The zone of stagnation and the mixing layer are characterized by super‐elevation, a lateral tilt is present at the edge of the mixing layer, and a zone of super‐elevation is present on the tributary side at the downstream junction corner. The latter seems associated with periodical upwelling and is not present in the numerical simulations that do not take into account instantaneous velocity fluctuations. Planform curvature, topographic steering related to the tributary mouth bar, and turbulent structures associated with the mixing layer all play a key role in the pattern of both the average and standard deviation of the water surface topography at confluences. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".