Step‐pool stability: Testing the jammed state hypothesis
Bibliographic record
Abstract
We investigate the stability of step‐pool channels by examining how the traditional approach to bed stability based on the critical Shields number is modified by particles jamming across the width of the channel. Experiments were conducted in a flume with slopes ranging between 3% and 18% and either smooth or rough walls. By varying the size of sediment and width of the flume we observed that the stability of the bed increases as the jamming ratio (channel width/D84step is the diameter at which 84% of the step stones are smaller) decreases for jamming ratios less than six. At low jamming ratios both grain‐on‐grain structuring and sediment entrainment phenomena affect the stability of the bed. Actual bed failure, however, depends upon the history of bed development and the chance arrangement of the stone structures in the bed. Thus, the experiments also demonstrate that the inherently stochastic nature of sediment transport affects not only the movement of individual grains but also the stability of the channel as a whole. Since stochastic processes affect the stability of the entire channel, there is no clearly defined separation between stable and unstable beds, rather, an overlapping field where both stable and unstable bed states can exist. This field was modeled using logistic regression to derive a probability of bed failure. A comparison of data from experiments with rough banks and smooth banks showed that rough banks significantly increase the stability of the 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".