Acoustic sampling effects on bedload quantification using acoustic Doppler current profilers
Bibliographic record
Abstract
This paper provides an evaluation of a hydro-acoustic technique for efficient quantification of the bedload transport in riverine environments. Stationary bedload measurements were conducted simultaneously at different study sites, using three different acoustic Doppler current profilers (ADCP) working at four different frequencies. The raw apparent bedload velocities were de-spiked and filtered in a streamwise direction. Then, functional correlations were observed between the magnitudes of the apparent velocities and the bedload transport rates measured by the conventional bedload sampler. Each ADCP yielded different results because of the different frequency backscatter sensitivity and acoustic penetration in the active bedload layer. In addition to the frequency, other acoustic parameters such as the percentage of the filtered data, transducers width, beam opening angle, beam-grazing angle and the pulse length, contributed to the acoustic bedload sampling. The influence of these parameters is examined, and recommendations are given for the performance and limitations of each instrument.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".