MUD, the next generation in acoustic backscatter sediment measurements
Bibliographic record
Abstract
Detection and classification of sediments within rarely occurring turbidity flows is challenging, in part because of the acoustic absorption and scattering within the dense suspended sediments. A new calibrated acoustic backscatter echosounder, the Multifrequency Ultrasonic Device (MUD™), was deployed from 13 to 16, May 2018, at about 120 m water depth within Bute Inlet, Canada. The MUD prototype (1.2 MHz, 769 kHz and 200 kHz) was deployed in a tautline mooring which included a passive acoustic recorder, an ADCP, CT sensor and OBS sensor. The frequencies for the prototype were selected to allow for a compromise between good acoustic range and penetration into dense flows with the potential for particle size discrimination. Preliminary analysis of the data indicates the presence of three turbidity flows over a two-hour period with speeds of up to 2.5 m/s. As anticipated, the lower frequencies did better in penetrating through the dense turbidity flow. This suggests that it will be possible to use inversion of the acoustic backscatter to estimate sediment concentrations within the dense head of the turbidity flow. Multifrequency inversion techniques will be applied to the less dense portion of the flow to estimate particle size distributions.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".