Analysis of the riverbed backscattered signal registered by ADCPs in different bedload transport conditions – field application
Bibliographic record
Abstract
Acoustic Doppler current profilers (ADCP) were deployed to investigate the backscattering (BS) signal in three navigable rivers, in different bedload transport conditions. This study aims to demonstrate that the BS strength, as an additional variable to the apparent bedload velocity, improves the characterization of the bedload transport using ADCPs. The M9 -3 MHz and the vertical beam M9 - 0.5 MHz showed decline of the BS strength as the bedload intensity increased, whereas the RDI -1.2 MHz was relatively insensitive. The correlation between the median grain size and the BS strength for the 0.5 MHz was linear, for the 3 MHz the BS strength was attenuated in the active layer, and for 1.2 MHz, it revealed a parabolic distribution. Moreover, the analyses of the ADCP measured variables, using wavelet transformations and unsupervised machine learning, highlighted the importance of the spatial and temporal variance and transient nature of the bedload transport.
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 | 0.001 |
| 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".