Spatial variability in dune morphology is driven by local flow patterns steered by bars and pools
Bibliographic record
Abstract
Bedform occurrence and geometry in sand-bedded rivers is traditionally predicted with phase diagrams and empirical equations, in which regional river characteristic are used. Field observations supporting these equations are often made in regions where bedform fields are known to be present and are spatially uniform. However, bedforms occurrence and geometry can vary significantly at the scale of the river width, limiting the applicability of bedform diagrams and questioning the objectivity of field study area selection. To enable the prediction of dune geometry, its spatial variability needs to be better understood. In this study, we aim to relate spatial variations in dune characteristic to grain size characteristics, river geometry, and local flow variation governed by the sub-bedform topography. We hypothesize that curvature-induced bars and pools drive local hydrodynamics, which in turn determine local dune characteristics. To test this hypothesis, bathymetric field data and sediment samples were collected in the fluvial-to-tidal-transition zone of the Fraser river, a sand-bedded lowland river in British Columbia, Canada. A 2D hydrodynamic model was created to explore the impacts of spatial variation in hydraulic conditions. We find that the cross-sectional variability in dune geometry is larger than the longitudinal variability, and that the transition of one type of dune field into another is abrupt rather than gradual. Phase diagrams do not capture these observations accurately. Local hydraulic conditions are more important in determining spatial variability in dune geometry than regional scale changes in river geometry, grain size variation and tidal influence. Dune height has an ambiguous relationship with river depth: the spatial variation in dune height depends on local shear stresses governed by the sub-bedform topography characterized by pools and troughs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".