Use of ADCPs for suspended sediment transport monitoring: An empirical approach
Bibliographic record
Abstract
Abstract A horizontally mounted 300 kHz acoustic Doppler current profiler was deployed in Fraser River at Mission, British Columbia, to test its capability to detect size‐classified concentration of suspended sediment. Bottle samples in‐beam provide a direct calibration of the hADCP signals. We also deployed a 600 kHz vertically mounted ADCP from a boat in combination with bottle samples. Fraser River at Mission is 525 m wide with moderate suspended sediment concentration (up to 350 mg L −1 in our measurements, mostly silt), and a modest sand load only at high flows. We use an entirely empirical approach to calculate the sediment load using ADCPs to test the reliability of acoustic methods when assumptions embedded in the sonar equation about the relation between suspended sediment size and concentration, and acoustic signals are violated. vADCP calibration using matched individual bottle samples and acoustic backscatter departed from the expected theoretical relation. Calibration using depth‐averaged sediment concentration and acoustic backscatter more closely matched theoretical expectations, but varied through the season. hADCP calibrations conformed with theoretical expectations and did not exhibit seasonal variation. Silt and sand were successfully discriminated; however, silt dominates the correlations. We found no coherent relation between acoustic attenuation and silt concentration. In‐beam results are extended by correlation to estimate mean sediment concentration and total suspended flux in the entire channel: this auxiliary correlation cancels any calibration bias and permits monitoring of size‐classified suspended sediment in absence of detailed information of sediment grain‐size 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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".