Experimental analysis of braided channel pattern response to increased discharge
Bibliographic record
Abstract
Physical models of gravel braided rivers were used to investigate the adjustment of braiding intensity to step changes in channel‐forming discharge and the mechanisms by which channel pattern adjustment and maintenance occurs. A braided channel developed at low discharge was subjected to two step increases in discharge between which the channel was given time to develop stable average braiding intensity in response to each steady discharge. Active (with visible bed material movement) and total channel networks were mapped throughout the experiment. Total braiding intensity exceeded active braiding intensity and both adjusted to a stable, average value at each discharge, indicating that channel pattern adjustment to total discharge involves both the active and the total network. Only portions of the total braided channel network developed at a given time, and it formed progressively by migration and avulsion of the (less extensive) active network. At equilibrium, the ratio of active to total braiding intensity stabilized at about 0.4. This stable value may increase with relative mobility of the bed material (stream power relative to grain size). The stable value was achieved via gradual increase of total braiding intensity while active braiding intensity adjusted very quickly to the increased flow. These adjustments are controlled by partial avulsion of the main active channel associated with changes in its sinuosity, and allocation of flow and bed load to secondary anabranches. Braided channel pattern dynamics is closely tied to, and explained by, the local dynamics and symmetry/asymmetry of bifurcations and avulsions.
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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) | 0.001 | 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".