Large grains matter: contrasting bed stability and morphodynamics during two nearly identical experiments
Bibliographic record
Abstract
Abstract While the stabilizing function of large grains in step‐pool streams has long been recognized, the role they play in gravel‐bed streams is less clear. Most researchers have ignored the role of large grains in gravel‐bed streams, and have assumed that the median bed surface size controls the erodibility of alluvial boundaries. The experiments presented herein challenge this convention. Two experiments were conducted that demonstrate the significant morphodynamic implications of a slight change to the coarse tail of the bed material. The two distributions had the same range of particle sizes, and nearly identical bulk d50 values (1.6 mm); however the d90 of experiment GSD1 was slightly finer (3.7 mm) than that for experiment GSD2 (3.9 mm). Transport rates during GSD1 were nearly four times greater than during GSD2 (even though the dimensionless shear stress was slightly lower), and the channel developed a sinuous pattern with well‐developed riffles, pools and bars. During GSD2 the initial rectangular channel remained virtually unchanged for the duration of the experiment. The relative stability of GSD2 seems to be associated with a slightly larger proportion of stable (large) grains on the bed surface: at the beginning of GSD1, 3.5% of the bed was immobile, while almost twice as much of it (6.1%) was immobile at the beginning of GSD2. The results demonstrate that the largest grains (not the median size) exert first‐order control on channel stability. Copyright © 2017 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".