Experiments on the grain size gap across gravel-sand transitions
Bibliographic record
Abstract
An abrupt transition in river bed grain size occurs from gravel to sand over a short downstream distance, often only a few channel widths, and is termed the gravel-sand transition. At this point, the bed structure also changes from framework- to matrix-supported. Whether the gravel-sand transition is externally imposed, a result of internal dynamics (sediment sorting, abrasion, suspension deposition) or due to some other emergent property is unclear. Interestingly, there is a general absence of rivers beds with median surface grain sizes between ~1 and 5 mm. Here we present a new global compilation of gravel-sand transition characteristics across a diverse range of settings. We identify commonalities in the location of gravel-sand transitions, finding they occur at upstream extents of externally imposed backwater effects, where the gravel supply is exhausted (i.e. downstream of mountain ranges), or where both effects are coincident. A series of laboratory channel experiments, examining changes in fluid and sediment dynamics across a gravel-sand transition, show systematic changes in near bed turbulence that control sand deposition patterns. Gravel coarser than ~10 mm prevents sand deposition at the bed surface. We also find that gravel-sand transitions cannot form where river beds contain substantial amounts of ~1 to 5 mm particles, because these grain sizes enhance the mobility of coarser gravel, preventing a shift to a sand bed.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".