High‐frequency streamflow acquisition and bed level/flow angle estimates in a mountainous river using shallow‐water acoustic tomography
Bibliographic record
Abstract
Abstract The acquisition of reliable discharge estimates is crucial in hydrological studies. This study demonstrates a promising acoustic method for measuring streamflow at high sampling rate for a long period using the fluvial acoustic tomography system (FATS). The FATS recently emerged as an innovative technique for continuous measurements of streamflow. In contrast to the traditional point/transect measurements of discharge, the FATS enables the depth‐averaged and range‐averaged flow velocity along the ray path to be measured in a fraction of a second. The field test was conducted in a shallow gravel‐bed river (0.9 m deep under low‐flow conditions, 115 m wide) for 1 month. The parameters (stream direction and bottom elevation) required for calculating the streamflow were deduced by a nonlinear regression to the discharge data from the well‐established rating curve. The cross‐sectional average velocities were automatically calculated from the acoustic data, which were collected on both riverbanks every 30 s. The FATS was connected to the internet so that the real‐time flow data could be obtained. The FATS captured discharge variations at a cut‐off frequency of approximately 70 day−1. The stream exhibited temporal discharge changes at multiple time scales ranging from a few tens of minutes to days. Copyright © 2016 John Wiley & Sons, Ltd.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".