Linking the spatial distribution of bed load transport to morphological change during high‐flow events in a shallow braided river
Bibliographic record
Abstract
Abstract This paper provides novel observations linking the connections between spatially distributed bed load transport pathways, hydraulic patterns, and morphological change in a shallow, gravel bed braided river. These observations shed light on the mechanics of braiding processes and illustrate the potential to quantify coupled material fluxes using remotely sensed methods. The paper focuses upon a 300 m long segment of the Rees River, New Zealand, and utilizes spatially dense observations from a mobile acoustic Doppler current profiler (aDcp) to map depth, velocity, and channel topography through a sequence of high‐flow events. Apparent bed load velocity is estimated from the bias in aDcp bottom tracking and mapped to indicate bed load transport pathways. Terrestrial laser scanning (TLS) of exposed bar surfaces is fused with the aDcp surveys to generate spatially continuous digital elevation models, which quantify morphological change through the sequence of events. Results map spatially distributed bed load pathways that were likely to link zones of erosion and deposition. The coherence between the channel thalweg, zone of maximum hydraulic forcing, and maximum apparent bed load pathways varied. This suggests that, in places, local sediment supply sources exerted a strong control on the distribution of bed load, distinct from hydraulic forcing. The principal braiding mechanisms observed were channel choking, leading to subsequent bifurcation. Results show the connection between sediment sources, pathways, and sinks and their influence on channel morphology and flow path directions. The methodology of coupling spatially dense aDcp surveys with TLS has considerable potential to understand connections between processes and morphological change in dynamic fluvial settings.
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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 | 0.001 | 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".