A two‐dimensional discrete particle model of gravel bed river systems
Bibliographic record
Abstract
The formation of bed forms in gravel bed rivers acts as a control on stream ecology and the response of rivers to floods. Available models do not reproduce the range of observed bed forms and do not consider interactions between the bed and flow hydraulics. The model presented here considers a gravel bed river as a complex system in which sediment clasts are represented as discrete elements. Simple and local rules describe the sediment and flow dynamics. Using a trimodal sediment distribution, irregular forms that scale with particle diameter develop without explicit feedback mechanisms because of the tendency of large particles to roll along the bed surface and collect into chains. Feedback mechanisms such as imbrication increase the effective entrainment threshold of groups of large particles and increase the stability of these imbricate forms. A second type of bed form is associated with saltating grains and emerges where particles are transported at a preferred distance. The development and maintenance of larger‐scale bed forms require feedback between the bed and flow properties. By allowing mean velocity to adjust to bed morphology and considering the effect of acceleration on turbulence generation and mean velocity profiles we demonstrate the emergence of forms similar in morphology to gravel sheets, dunes, and riffle pools. The model is best used to complement field‐based studies and is suitable for testing hypotheses of streambed behavior.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".