Fate of the grain size gap material in river bed sediments
Bibliographic record
Abstract
There is a paucity of rivers beds with median surface grain sizes between ~1 and 5 mm, a range also referred to as the grain size gap. The grain size gap corresponds to the rapid reduction in grain size associated with the gravel-sand transition in river systems, where grain sizes reduce from >5 mm to 1 mm over a downstream distance equivalent to just a few channel widths. In existing models and experiments, these grain sizes must typically be omitted to generate the abrupt reduction in grain size across the transition. However, there is evidence that these grain sizes are present in river systems and hillslope sediment supplies. We present a series of new laboratory experiments in a narrow flume, examining the fate of grain size gap material in both sediment feed and bed distributions. Our observations indicate that where sand falls out of suspension at the upstream end of the gravel-sand transition, grain size gap material in gravel beds experiences enhanced mobility. We propose that this occurs through a geometric effect where medium sand is the exact size to bridge interstitial pockets in fine gravel bed surfaces. We hypothesize this effect could enhance grain protrusion of fine gravel and increases the likelihood of entrainment, or that sand deposition smooths the bed, generating fluid acceleration in the near-bed flow region. Grain size gap particles cannot form the dominant mode in river bed surface sediments because sand destabilizes particles of this size.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".