Site specificity of bed load measurement using an acoustic Doppler current profiler
Bibliographic record
Abstract
Concurrent measurements of bed load transport velocity (v) from the bottom tracking feature of an acoustic Doppler current profiler (aDcp) and bed load transport rate (gb) from conventional pressure difference samplers are presented. Data sets were collected from both gravel bed and sand bed reaches of the Fraser River, covering a bed material range of 0.25–25 mm. Strong relations, in which v explained >70% of the variability of measured gb, were observed in a gravel bed and a sand bed reach. Differences in correlation between v and gb among the contrasting environments are attributed to variations in both the bed load particle size and aDcp operating parameters. Similar values of v were associated with lower mass transport rates in a sand bed than in a gravel bed reach. A nondimensional data collapse, accounting for differences in bed load particle size, explained 42% of the observed variance of the combined data set. Longer averaging times were required in the gravel bed reach, likely due to the stochastic nature of bed load entrainment in gravels and the resulting heterogeneous bed load velocity field. Bed load transport was modeled using both shear stress models and a kinematic model that utilizes the estimated bed load velocity. The standard shear stress models provided poor matches to the measured bed load transport rates: the sand bed data were overpredicted, and the gravel bed predictions correlated poorly with the measured predictions. Use of the kinematic model yields an estimate for the product of bed load concentration and bed load layer depth. This work highlights the potential of acoustic techniques for estimating bed load.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".