Bedload Transport Velocity: Finding the Signal Amidst the Noise
Bibliographic record
Abstract
A method is presented to estimate the probability density function of bedload velocity from noisy stationary data. 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). This paper describes a deconvolution procedure to estimate the probability density functions for the actual signal and the noise. The procedure involves the optimization of a brute-force computational summation of random variables for the acoustic noise (assumed Gaussian with zero mean) and the spatially averaged bedload velocity within the insonified area of each acoustic beam (V). Two possible distributions for V were evaluated; a semi-theoretical compound Poisson-gamma (cPg) distribution, and an empirical gamma distribution. We tested this procedure on two aDcp time series, measured in two different gravel-bed rivers (Fraser River and Norrish Creek). Models generated using both the cPg and gamma distributions for V fit both data sets very well: the modeled convolution distribution did not differ significantly from the distribution of the original data. The gamma distribution fit slightly better than the cPg. Optimized bedload speed distributions (fV) were highly left skewed indicating that the bedload speed averaged within a beam area tended to be mostly near zero with a few high values, as was expected for partial bedload transport of gravel, where most of the particles remain at rest most of the time. The acoustic noise was comparable to acoustic noise for aDcp water velocity measurements, which is an order of magnitude greater than typical bottom tracking noise.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".