Breaking from the average: Why large grains matter in gravel‐bed streams
Bibliographic record
Abstract
Abstract While the influence of large grains on the morphodynamics of gravel‐bed rivers has long been recognized, nothing dominates our collective efforts to model such rivers like the bed surface D50, which turns up in virtually all the relevant equations. While researchers interested in flow resistance have recognized the relative importance of large grains and have modified flow resistance equations accordingly, there have been few attempts to quantify the effects of large grains on gravel‐bed river morphodynamics. However, there is little evidence that D50 exerts first‐order control over the physics occurring along the channel boundary, and its prevalence seems to be primarily based on the untested, a priori assumption that the best description of a distribution is the mean or median value. This commentary questions the long‐standing assumption that D50 is the best choice for characteristic grain size, and uses evidence from previous studies to show that mobilization of the largest grains in the bed likely controls morphological stability, and possibly sediment transport. © 2018 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".