Spatial Distribution of Bedload Transport Velocity Using an Acoustic Doppler Current Profiler
Bibliographic record
Abstract
Maps are presented of the spatial distribution of two dimensional bedload transport velocity vectors. Bedload transport determines the morphodynamics of fluvial and coastal environments. However, bedload remains poorly understood, in part due to inadequate sampling techniques that can not measure the spatial and temporal variability of bedload. We have been developing a new technique to measure bedload transport velocity in the field, using the bottom tracking feature of acoustic Doppler current profilers (aDcps). If a differential global positioning system (DGPS) is used concurrently, then estimates of the apparent velocity of bedload can be gained at a nominal rate of 1 Hz from a moving boat. The apparent velocity should be a measure of the mean velocity of the bed surface. Data were collected using an aDcp within a selected area near the Agassiz-Rosedale bridge in the gravel-bed reach of the Fraser River, as well as in the sand-bed Sea Reach of the Fraser River delta. Two dimensional vector maps were generated of the spatial distribution of measured bedload transport velocity. Bedload velocity vectors interpolated onto a uniform grid revealed coherent patterns in the bedload velocity distribution. Concurrent Helley-Smith bedload trap sampling in the sand-bed reach corroborated the trends observed in the bedload velocity map. Contemporaneous 2D vector maps of near-bed velocity (velocity in bins centered between 25 cm and 50 cm from the bottom) were also generated from the aDcp water velocity data. Using a vector correlation coefficient, which is independent of the choice of coordinate system, the bedload velocity distribution was shown to be significantly correlated to the near-bed velocity distribution. The bedload velocity distribution also compared favorably with variations in depth and estimates of the spatial distribution of shear stress.
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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 | 0.002 | 0.002 |
| 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".