Mapping spatial distributions and uncertainty of water and sediment flux in a large gravel bed river reach using an acoustic Doppler current profiler
Bibliographic record
Abstract
Spatial distributions of depth‐averaged water velocity, shear velocity, and apparent bed load velocity are mapped for the first time in a long reach of a wandering gravel bed river, lower Fraser River, British Columbia. Spatially intensive acoustic Doppler current profiler (aDcp) measurements were collected on the falling limbs of two freshets. Flow in the first year was near the threshold of motion, whereas in the second year discharge exceeded bankfull. Spatial distributions are interpolated from the point data using kriging. Joint density functions for shear velocity and flow depth throughout the reach are presented; marginal densities for shear velocity were near normally distributed but depth distributions were positively skewed by deep pools. The uncertainty of the spatial distributions is also assessed based on modeled temporal variability of the flow and bed load transport, measured aDcp error velocities, and calculated interpolation errors. The resulting maps are remarkably coherent, with maximum depth‐averaged velocity, shear velocity, and apparent bed load velocity following the thalweg. Largest values occur in channel bends at zones of flow convergence where the thalweg flow accelerates toward the bank. However, in the lower flow year the highest apparent bed load velocity was observed outside the thalweg in a deep pool downstream of a rapidly eroding cut bank. Erosion at this site was related to a flow confluence with relatively low shear but highly turbulent, strongly three‐dimensional separated flow.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".