Flow Strength and Bedload Sediment Travel Distance in Gravel Bed Rivers
Bibliographic record
Abstract
Abstract The quantification of bedload sediment transport in rivers is possible from statistics of individual particle displacements. However, there is a lack of empirical basis for a universal relation between particle displacement distance and hydraulic drivers. Previous work suggests that a simple linear relation exists between the energy of a flood and the mean travel distance of bedload particles. Such a relation would be advantageous, but a consistent model able to collapse the data from different rivers has not been developed. Here, we develop a predictive relation from a unique data set collected in three watersheds from a single region but contrasting hydrologic regimes due to urbanization and storm water management. Additional data from two rivers from outside the region are used to validate the model. We show that the mean of an exponential distribution of surface particle travel displacements can be reliably predicted from either the cumulative discharge or stream power exceeding the mobilization threshold, which is calibrated using field data. The strength of the relation decreases after large flood events that appear to cause tracer burial due to vertical mixing. This result indicates that the relation is most applicable for the entrainment phase of transport in which tracers are dispersing over the bed surface. Tracer movements become more challenging to predict over a long series of events due to burial and eventual tracer slowdown, but the relation remains valid for the particles located on the bed surface, making it suitable for analyzing the impact of climate and landscape changes over time.
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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 | 0.002 |
| 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 | 0.000 | 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".