Acoustically‐Derived Sediment‐Index Methods in Large Rivers: Assessment of Two Acoustic Inversion Models
Bibliographic record
Abstract
Abstract In large rivers across the world, measurement of sediment in transport can be challenging and expensive to collect, yet monitoring and measurement in these systems is critical to our management of these systems in the face of climate change and rising sea levels and because its movement with channels provides natural resilience of riverine, estuarine, and near shore environments. As such, developing programs for continuous monitoring of sediment is necessary. Here we present and assess acoustic methods used to estimate suspended sediment entering the Lower Fraser River. The channel is 550 m wide, which is greater than the range of acoustic instruments—a common challenge in large rivers. We demonstrate a sediment‐index methodology that relates an acoustically‐derived concentration estimate to channel‐average concentration. We compare two different acoustic methods (multifrequency and single frequency) as acoustic index estimators. Between 2012 and 2014 we conducted 25 sampling campaigns where cross‐section, point‐integrated suspended sediment samples, and samples from within the acoustic range were collected. Annual flux was computed for the 3 years using both methods and compared to a more traditional sediment supply to discharge rating‐curve. The most robust method used a single‐frequency inversion where the sum of acoustically‐derived, fractional sand and silt/clay estimates equated the total flux. Development of such monitoring programs will aid managers, engineers, and scientists in policy and practice, emboldening our ability to observe, adapt, and predict a changing environment.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".