Grain sorting in the morphological active layer of a braided river physical model
Bibliographic record
Abstract
Abstract. A physical scale model of a gravel-bed braided river was used to measure vertical grain size sorting in the morphological active layer aggregated over the width of the river. This vertical sorting is important for analyzing braided river sedimentology, for numerical modeling of braided river morphodynamics, and for measuring and predicting bedload transport rate. We define the morphological active layer as the bed material between the maximum and minimum bed elevations at a point over extended time periods sufficient for braiding processes to rework the river bed. The vertical extent of the active layer was measured using 40 hourly high-resolution DEMs (digital elevation models) of the model river bed. An image texture algorithm was used to map bed material grain size of each DEM. Analysis of the 40 DEMs and texture maps provides data on the geometry of the morphological active layer and variation in grain size in three dimensions. By normalizing active layer thickness and dividing into 10 sublayers, we show that all grain sizes occur with almost equal frequency in all sublayers. Occurrence of patches and strings of coarser (or finer) material relates to preservation of particular morpho-textural features within the active layer. For numerical modeling and bedload prediction, a morphological active layer that is fully mixed with respect to grain size is a reliable approximation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".