Monitoring Tidal Bores using Acoustic Tomography System
Bibliographic record
Abstract
Kawanisi, K.; Zhu, X.-H.; Fan, X., and Nistor, I., 2017. Monitoring tidal bores using acoustic tomography system.Continuous measurements of flow velocities and suspended sediments were carried out in the Qiantang River (China) with extreme tidal bore conditions. Fluvial and coastal acoustic tomography systems (FATS/CATS) were used in the field studies simultaneously with acoustic Doppler current profilers (ADCPs) and optical backscatter (OBS) equipment. A couple of broadband transducers were installed diagonally across the river around 90 km upstream of the mouth. The length of the sound transmission line was 3050 m. Cross-sectional averaged velocities (V) collected by FATS/CATS enabled the authors to estimate important characteristics of the tidal bores (bore height and celerity). The changes in V recorded during the upstream movement of the bore ranged from 1.35 to 1.76 ms−1. The height and celerity of the bore varied from 1.0 to 1.32 m and 7.59 to 8.29 ms−1, respectively. Since a point/vertical measurement cannot represent a river's cross section, the ADCP data for velocity and water level (pressure) near the riverbank underestimated the bore height by 22% and 16%, respectively. The maximum suspended sediment concentration (SSC) was observed to have occurred approximately 1 h after the bore's arrival; the time lag between the maximum SSC and the bore front is considerably larger than the time lags in previous works. In the case of larger bores, the section mean SSCs, which were deduced from the sound attenuation of FATS/CATS due to suspended sediment, appeared to be appropriate. The time history of the velocity, measured by ADCPs, during the passage of the bore deviated from the normal velocity profile, i.e. the velocity magnitude decreased with an increasing height above the bed for the relaxation time of a few minutes following the arrival of the bore.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".