Application of Multifrequency Acoustic Inversions Using Three Horizontally Profiling ADCPs
Bibliographic record
Abstract
Abstract An array of three horizontally mounted acoustic Doppler current profilers (ADCPs) deployed in the Fraser River at Mission British Columbia was used to calculate suspended sediment concentration (M), mean grain size (Dg), and geometric standard deviation σg of the grain size distribution (GSD) using acoustic signal inversion. We examine two different multifrequency methods, an explicit and implicit inversion, by systematically allowing M, Dg, and GSD standard deviation (σg) to vary in the inversions. This is the first application of these methods to commercially produced ADCPs, and we further develop methods that use both viscous and scattering attenuation. We couple these inversions with bottle samples collected within the ensonified volume that were analyzed for suspended sediment concentration and GSD characteristics. Concentrations ranged from 20 to 350 mg/L with GSD characteristics dominated by silt to fine sand. We estimate the necessary calibration parameters needed for complete acoustic inversions and find that M and Dg are both dependent on the calibration constant, which could be a source of error in multifrequency inversions using uncalibrated ADCPs. Comparisons between the inversion results and samples show that the implicit method tends to perform best at all flows for estimating M while providing realistic estimates of grain size at high flows only. The explicit method performed well at high flows, but poorly at low flows, for M and Dg. Estimates of σg using both inversion methods indicate frequency dependence. We show that the daily averaged acoustically derived M and Dg capture the hysteretic nature of sediment transport in the Fraser River.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".