Linkages between bedload displacements and topographic change
Bibliographic record
Abstract
Summary Changes in bed topography that build and maintain channel morphology are driven by the displacements of individual particles, either though their entrainment or deposition. However, the linkages between these topographic changes and individual grain displacements have not been comprehensively addressed, as many historical tracer studies have not included coincident topographic data. In this study, we compare the movements of bedload tracers to the differences in repeat topographic surveys across four gravel‐bed river reaches. To do this, we apply a 1‐D Bayesian survival process model to the starting and ending locations of tracers. This model estimates downstream trapping probabilities, which represent the likelihood that a given segment of channel will “trap” an entrained particle. We then adapt this model to estimate downstream trapping probabilities using digital elevation models of difference and compare the results. The estimates from the tracer and topographic trapping models showed general alignment, meaning that tracers were preferentially trapped in segments that experienced deposition along the channel. Thus, tracers in this study were able to identify downstream differences in bedload transport. The comparison also highlighted that tracer‐estimated trapping probabilities were larger than topographically estimated ones. This supports previous observations that sediment travel distances estimated using tracers are shorter than those estimated using morphological methods. We find that the differences between these two estimates vary systematically across study environments. These variations are attributable to either study design (i.e., tracers being larger than the median size of the sediment that deforms the bed) or differences in compensating scour and fill. We explore potential causes for differences in compensating scour and fill, including hydrograph shape, sediment delivery regime, channel deformation style, and channel width, highlighting that morphodynamics needs to be considered in designing bedload tracer studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".