Measurement of the spatial distribution of fluvial bedload transport velocity in both sand and gravel
Bibliographic record
Abstract
Abstract Maps are presented of the spatial distribution of two‐dimensional bedload transport velocity vectors. Bedload velocity data were collected using the bottom tracking feature of an acoustic Doppler current profiler (aDcp) in both a gravel‐bed reach and a sand‐bed reach of Fraser River, British Columbia. Block‐averaged bedload velocity vectors, and bedload velocity vectors interpolated onto a uniform grid, revealed coherent patterns in the bedload velocity distribution. Concurrent Helley‐Smith bedload sampling in the sand‐bed reach corroborated the trends observed in the bedload velocity map. Contemporaneous 2D vector maps of near‐bed water velocity (velocity in bins centered between 25 cm and 50 cm from the bottom) and depth‐averaged water velocity were also generated from the aDcp data. Using a vector correlation coefficient, which is independent of the choice of coordinate system, the bedload velocity distribution was significantly correlated to the near‐bed and depth‐averaged water velocity distributions. The bedload velocity distribution also compared favorably with variations in depth and estimates of the spatial distribution of shear stress. Published in 2004 by John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".