Morphology, bedload and sedimentology of active gravel bed rivers
Bibliographic record
Abstract
Morphology, bedload and sedimentology of morphologically active gravel bed rivers interact in fundamental ways. In braided and wandering rivers these interactions have distinct characteristics. In these cases much of the bedload transfer is tied up in morphological change so that the bar and channel scale morpho-dynamics are, in effect, the bedload transport process. Physical models and field data reveal several inter-related aspects of this interaction. We can define the morphological active layer as that in which erosion, deposition and bed particle exchange occur during channel-forming flows. The dimensions, complexity, and lateral and longitudinal connectivity of this layer increase with discharge in a given river and with channel-forming stream power between rivers. Bedload flux correlates strongly with the dimensions of the active layer and temporal variability of bedload at a given discharge is a consequence of bar-scale variation in morphological change in complex morphology. Rates of planimetric change in braided channels also follow this morphological-bedload relationship. Higher rates of morphological change also correlate with greater bed material mobility, approaching equal mobility at the highest rate of change and the highest morphological active layer dimensions. Bed particle transfer distances and burial depths are also strongly controlled with the length scale and depth of the bar-scale morphology and active layer. The sedimentology reflects the channel morphological scale and processes in defining sedimentary unit thicknesses and geometry. The deposits of the active channel belt are almost homogenous with respect to particle size because of the ‘turnover’ of the bed material. Morphology, bedload and sedimentology of morphologically active gravel bed rivers interact in fundamental ways that help to define the characteristics of these channel types. To what extent are these observations applicable in other channel types?
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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 | 0.001 | 0.001 |
| 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".