Experimental insights into the effect of event sequencing and sediment input texture on step‐pool channel evolution
Bibliographic record
Abstract
Abstract We conducted flume experiments in a step‐pool channel with different sediment supply regimes to explore how the sequencing and the grain‐size of sediment pulses affect channel evolution. Our results show that the sequencing of events is not a primary control as the trends in flow characteristics, bedload transport, sediment storage, step evolution, and step frequency were similar in feed phases with different sequencing of sediment pulses. Channel adjustment and step stability in a short timescale (i.e., in a 4‐h run) were mostly controlled by the magnitude and frequency of sediment pulses. A coarser sediment feed mixture led to a coarser bed surface which increased both sediment storage in the channel and step stability. Our experiments show that channel evolution in a step‐pool channel is primarily controlled by the magnitude and frequency of sediment pulses and the grain size of sediment supply rather than the event sequencing of sediment pulses.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".