Application of acoustic tomography to reconstruct the horizontal flow velocity field in a shallow river
Bibliographic record
Abstract
Abstract A novel acoustic tomographic measurement system capable of resolving sound travel time in extremely shallow rivers is introduced and the results of an extensive field measurements campaign are presented and further discussed. Acoustic pulses were transmitted over a wide frequency band of 20–35 kHz between eight transducers for about a week in a meandering reach of theBāsen River, Hiroshima, Japan. The purpose of the field experiment was validating the concept of acoustic tomography in rivers for visualizing current fields. The particular novelty of the experiment resides in its unusual tomographic features: subbasin scale (100 m × 270 m) and shallowness (0.5–3.0 m) of the physical domain, frequency of the transmitted acoustic signals (central frequency of 30 kHz), and the use of small sampling intervals (105 s). Inverse techniques with no a priori statistical information were used to estimate the depth‐average current velocity components from differential travel times. Zeroth‐order Tikhonov regularization, in conjunction with L‐curve method deployed to stabilize the solution and to determine the weighting factor appearing in the inverse analysis. Concurrent direct environmental measurements were provided in the form of ADCP readings close to the right and left bank. Very good agreement found between along‐channel velocities larger than 0.2 m/s obtained from the two techniques. Inverted quantities were, however, underestimated, perhaps due to vicinity of the ADCPs to the banks and strong effect of river geometry on the readings. In general, comparing the visualized currents with direct nodal measurements illustrate the plausibility of the tomographically reconstructed flow structures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".