Bed topography and the development of forced bed surface patches
Bibliographic record
Abstract
Channel topography in gravel‐bedded rivers interacts with the local flow and sediment transport fields to produce “forced patches,” which are temporally stable areas on the bed that display similar grain size and sorting. In an effort to enhance our empirical understanding of the mechanisms responsible for the formation of forced patches, we conducted a near‐field scale flume experiment in which a large (55 m long, 2.74 m wide), straight, sediment recirculating flume was provided a constant water discharge and a unimodal sediment mixture ranging in size from 2 to 45 mm. A sequence of alternate bars developed and became essentially fixed in space, producing quasi‐steady state bed topography over which we made measurements of local near‐bed velocity and sediment transport. The bed developed temporally and spatially persistent forced patches with a general pattern of coarse bar tops and fine pools, which we characterized by visual mapping and with the application of an automated image processing procedure to a high‐resolution (1 × 1 mm) digital elevation model of the bed surface. The boundary shear stress field, calculated from velocity measurements and with a quasi‐three‐dimensional hydraulic model (FaSTMECH), displayed substantial variability across the bar unit. Bed surface grain size did not correlate with local boundary shear stress; instead, topographically forced divergences in the boundary shear stress field were matched by divergences in the sediment transport field. The resultant cross‐stream sediment flux was size‐selective that in turn forced a bed surface textural response leading to coarse bar tops and fine pools. Our observations suggest that size‐selective cross‐stream bed load transport is a mechanism responsible for the development of forced bed surface patches in gravel bed channels that have topographically forced heterogeneous flow fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".