Estimating Bedload in Sand‐Bed Channels Using Bottom Tracking from an Acoustic Doppler Profiler
Bibliographic record
Abstract
A method to estimate bedload in sand-bed rivers using bottom tracking from an acoustic Doppler profiler (ADP) is outlined and tested. The velocity of a mobile sand bed is related to the difference between the ‘apparent’ boat velocity with respect to the bed measured with ADP bottom tracking and the ‘actual’ boat velocity measured with a differential global positioning system (DGPS). Under simple assumptions the velocity of a mobile sand bed can be converted to an estimate of bedload. Estimates of bedload using this method were acquired from a launch anchored over the upper stoss face of large sand dunes in the Fraser River, British Columbia. These estimates are compared with measurements from a Helley–Smith sampler and predictions from the Van Rijn bedload formula. All three methods produce consistent and comparable values. Near concordance was observed between the mechanical and acoustic estimates of bedload transport, although with a large degree of scatter, indicating that both instruments are measuring similar fractions of near-bed transport. In comparison, the correlation with computed transport, although stronger, was more strongly biased. Part of the difficulty in reconciling the measurements lies in the arbitrary nature of the division between ‘bedload’ and near-bed suspension in transport over a sand bed. Within this constraint, ADP technology shows potential to yield remote measurements of bedload in sand-bed channels, escaping the limitations of mechanical samplers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".