Response of low‐angle dunes to variable flow
Bibliographic record
Abstract
Abstract Current understanding of bedform dynamics is largely based on field and laboratory observations of bedforms in steady flow environments. There are relatively few investigations of bedforms in flows dominated by unsteadiness associated with rapidly changing flows or tides. As a consequence, the ability to predict bedform response to variable flow is rudimentary. Using high‐resolution multibeam bathymetric data, this study explores the dynamics of a dune field developed by tidally modulated, fluvially dominated flow in the Fraser River Estuary, British Columbia, Canada. The dunes were dominantly low lee angle features characteristic of large, deep river channels. Data were collected over a field ca 1·0 km long and 0·5 km wide through a complete diurnal tidal cycle during the rising limb of the hydrograph immediately prior to peak freshet, yielding the most comprehensive characterization of low‐angle dunes ever reported. The data show that bedform height and lee angle slope respond to variable flow by declining as the tide ebbs, then increasing as the tide rises and the flow velocities decrease. Bedform lengths do not appear to respond to the changes in velocity caused by the tides. Changes in the bedform height and lee angle have a counterclockwise hysteresis with mean flow velocity, indicating that changes in the bedform geometry lag changes in the flow. The data reveal that lee angle slope responds directly to suspended sediment concentration, supporting previous speculation that low‐angle dune morphology is maintained by erosion of the dune stoss and crest at high flow, and deposition of that material in the dune trough.
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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 | 0.000 | 0.000 |
| 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".