Bedload path length and point bar development in gravel‐bed river models
Bibliographic record
Abstract
Abstract Low‐sinuosity meandering gravel‐bed flume experiments were employed to investigate spatial patterns of deposition, which point to patterns of channel development related to the pool and bar morphology. At channel‐forming discharges, fluorescent bedload tracers indicate that deposition is typically focused around the point bar apex, downstream of the apex (contributing to downstream bar migration), and at the bar head/riffle surface. Seven flume experimental runs illustrate a sequence of point bar development related to the spatial patterns of tracer deposition, and the related path length distribution. At early stages of bar formation, transport is from the scour zone across the point bar head to the bar apex and bar margin downstream of the apex. As the point bar develops, bedload transport across the bar decreases, as transport along the channel thalweg increases and sediment is deposited along the bar margin. Deposition cells appear to move from downstream to upstream of the bar apex as this sequence of bar formation progresses. At low (non‐channel‐forming) discharges, transport occurs to the bar head/riffle surface with very little material being transported to the apex region or point bar interior. The implication is that there is an inherent connection between the loci of particle deposition and point bar formation, largely controlled by the morphology of the channel.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".