Evaluation of aDcp processing options for secondary flow identification at river junctions
Bibliographic record
Abstract
Abstract Secondary circulation in river confluences results in a spatial and temporal variation of fluid motion and a relatively high level of morphodynamic change. Acoustic Doppler current profiler (aDcp) vessel‐mounted flow measurements are now commonly used to quantify such circulation in shallow water fluvial environments. It is well established that such quantification using vessel‐mounted aDcps requires repeated survey of the same cross‐section. However, less attention has been given to how to process these data. Most aDcp data processing techniques make the assumption of homogeneity between the measured radial components of velocity. As acoustic beams diverge with distance from the aDcp probe, the volume of the flow that must be assumed to be homogeneous between the beams increases. In the presence of secondary circulation cells, and where there are strong rates of shear in the flow, the homogeneity assumption may not apply, especially deeper in the water column and close to the bed. To reduce dependence on this assumption, we apply a newly‐established method to aDcp data obtained for two medium‐sized (~60–80 m wide) gravel‐bed river confluences and compare the results with those from more conventional data processing approaches. The comparison confirms that in the presence of strong shear our method produces different results to more conventional approaches. In the absence of a third set of fully independent data, we cannot demonstrate conclusively which method is best, but our method involves less averaging and so in the presence of strong shear is likely to be more reliable. We conclude that it is wise to apply both our method and more conventional methods to identify where data analysis might be impacted upon by strong shear and where inferences of secondary circulation may need to be made more cautiously. © 2019 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".