Doppler sonar measurements of bedload transport: Evaluations using computer models and lab trials
Bibliographic record
Abstract
Studies have demonstrated that bottom velocity measurements from Doppler sonar systems are proportional to bedload transport rates. Given the complexity of acoustic backscatter and sidelobe interactions near the bottom boundary, the exact source of this signal is not obvious. We explore this measurement using a computer simulation and a series of laboratory trials. The system that we have developed for these studies, MFDop, is a multi-frequency (1.2-2.2 MHz), bistatic Doppler sonar that provides 3-component velocity profiles over a ~ 30 cm profile with ~5 mm resolution at a rate of 50 profiles/sec. Model simulations show that side-lobe contamination biases the velocity measurements above the bottom but that effect is reduced in the bedload layer itself. We report on tests of our system in field conditions at the St. Anthony Falls Laboratory (SAFL). The SAFL facility provides a 1.8 m deep, 2.75 m wide flume tank. In our trials, we used a 1 m depth flow of about 1 m/s over a mobile bed of sand with median grain size d50 = 0.4 mm. We find agreement between transport estimates determined using the MFDop, the sediment trap system integrated in the SAFL flume, and estimates based on bedform migrations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".