Calculation of in situ acoustic sediment attenuation using off‐the‐shelf horizontal <scp>A</scp>DCPs in low concentration settings
Bibliographic record
Abstract
Abstract The use of “off‐the‐shelf” acoustic Doppler velocity profilers (ADCPs) to estimate suspended sediment concentration and grain‐size in rivers requires robust methods to estimate sound attenuation by suspended sediment. Theoretical estimates of sediment attenuation require a priori knowledge of the concentration and grain‐size distribution (GSD), making the method impractical to apply in routine monitoring programs. In situ methods use acoustic backscatter profile slope to estimate sediment attenuation, and are a more attractive option. However, the performance of in situ sediment attenuation methods has not been extensively compared to theoretical methods. We used three collocated horizontally mounted ADCPs in the Fraser River at Mission, British Columbia and 298 observations of concentration and GSD along the acoustic beams to calculate theoretical and in situ sediment attenuation. Conversion of acoustic intensity from counts to decibels is influenced by the instrument noise floor, which affects the backscatter profile shape and therefore in situ attenuation. We develop a method that converts counts to decibels to maximize profile length, which is useful in rivers where cross‐channel acoustic profile penetration is a fraction of total channel width. Nevertheless, the agreement between theoretical and in situ attenuation is poor at low concentrations because cross‐stream gradients in concentration, sediment size or GSD can develop, which affect the backscatter profiles. We establish threshold concentrations below which in situ attenuation is unreliable in Fraser River. Our results call for careful examination of cross‐stream changes in suspended sediment characteristics and acoustic profiles across a range of flows before in situ attenuation methods are applied in river monitoring programs.
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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".