Remote acoustic measurement of the velocity within water-immersed gravity-driven granular flows
Bibliographic record
Abstract
Measuring bedload transport at high spatial and temporal resolution in energetic aqueous environments is challenging. Acoustic remote-sensing technologies are attractive because the measurement can be made without disturbing the mobile bed or the near-bed flow. Of particular interest is the development of broadband MHz-frequency acoustic systems capable of simultaneous measurements of backscatter amplitude and phase at mm-scale range resolution and 100 Hz sampling frequencies. Using such an instrument, we study granular flow in a water-submerged rectangular chute. By releasing sediments in the upstream portion of the chute, a O(1)cm-thick layer of avalanching sediment is produced. Trials were carried out for both erodible and fixed roughness beds. Natural sand and glass beads with median grain sizes ranging from 0.22 to 0.4 mm were used. The thickness of, and velocity profile within, the moving layer were measured using a wide bandwidth coherent Doppler profiler operating at 1.2 MHz. The velocity profiles are compared to estimates made with video imagery through the chute sidewall. The velocities at the sediment-water interface are compared to estimates made with a commercially available Doppler profiler (Vectrino) operating at 10 MHz and with imagery from a submerged video camera.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".