Evaluation of an acoustic Doppler technique for bed-load transport measurements in sand-bed Rivers
Bibliographic record
Abstract
The bottom tracking (BT) feature of the acoustic Doppler current profilers (ADCP) have emerged as a promising technique in evaluating the bed load. Strong statistical correlations are reported between the ADCP BT velocity and the transport rate obtained by physical sampling or dune tracking; however, these relations are strictly site-specific and a local calibration is necessary. The direct physical sampling is very labor intensive and it is prone to high instrument uncertainty. The aim of this work is to develop a methodology for evaluating the bed load transport using commercial ADCPs without calibration with physical samples. Relatively long stationary measurements were performed in a sand-bed and sand gravel rivers, using three different ADCPs working at 3MHz, 1.2MHz and 0.6MHz. Simultaneously, bed load samples were collected with physical samplers, and the riverbed was closely observed with digital cameras mounted on the samplers. It is demonstrated that the kinematic transport model can yield a relatively good estimate of the transport rate by directly using filtered apparent velocity, the knowledge of the hydraulic conditions and instrument-related calibration coefficients. Additionally, the ADCP data can help in qualitative assessment of the physical sampling. Future investigation of the backscattering echo and further confirmation of the BT apparent velocity should be performed in laboratory-controlled conditions.
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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 | 0.000 |
| Open science | 0.001 | 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".