Deconvolution Technique to Separate Signal from Noise in Gravel Bedload Velocity Data
Bibliographic record
Abstract
A deconvolution procedure is presented to estimate the probability density function of bedload transport velocity from noisy stationary data collected using the bottom tracking feature of acoustic Doppler current profilers (aDcps). The procedure involves the optimization of a computational summation of random variables for the instrument noise (assumed to be Gaussian with zero mean) and the spatially averaged bedload velocity within the insonified area of each acoustic beam (V). The procedure was tested on two aDcp time series, measured in two different gravel-bed rivers (Fraser River and Norrish Creek). Models generated using either a semitheoretical compound Poisson-gamma distribution or an empirical gamma distribution for V were similar and did not differ significantly from the distribution of the original data. Optimized distributions for V were highly positively skewed. The instrument noise was comparable to instrument noise for aDcp water velocity measurements, i.e., an order of magnitude greater than typical bottom tracking noise. The deconvolution procedure presented herein is generally applicable for the difficult measurement problem of determining the actual signal distribution when measurements are contaminated by noise, particularly for the case of positive-valued signal contaminated by Gaussian noise. The procedure produced the first field estimates of spatially averaged bedload velocity distribution.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".