A river's connective tissue: Lab observations of particle pathways and riffle formation during floods
Bibliographic record
Abstract
In rivers it is difficult to quantify bedform dynamics during storm events. Direct observation of \nsediment pathways would provide insight into the mechanisms that underly bedform formation and \ndestruction. In the current study, our objective was to visualize these processes in a meandering pool riffle system with partial bed cover. Observing erosive and depositional patterns, as well as the locations \nof active sediment transport, provides insight into the validity of various pool-riffle maintenance \ntheories. We used a physical 1:40 scaled model of Toronto’s Wilket Creek to simulate storm events \nduring which riffles formed as connective bedforms between alternate point bars. Exported sediment \nwas weighed and sieved to measure the grain size distribution, while the bed’s pre- and post- storm \ntopography was quantified using Structure-from-Motion techniques. Sediment pathways were observed \nusing a novel technique, where regions of interest were filmed at 60 frames per second under ultra violet light, illuminating painted tracers. Three paint colors were used for different size tracers, which \nallowed us to apply image segmentation and create separate videos for three size fractions of the \nsediment. Pathways were then extracted using Lagrangian tracking software. Results show that the area \nof active transport is limited to a narrow portion of the channel width that increases with flood stage. \nAt low flow, transport is routed along the toe of point bars, while no particles travel into the region of \nthe pool, where the bed is uncovered. Riffles are rarely observed at these stages. As the flow increases, \nthe lateral extent of active transport expands to include the higher parts of the bars, while connective \nriffles grow in areal extent and height. Erosion and deposition was found to occur more readily along \nthe active sediment transport zones. Pathways varied by particle size so that smaller particles traveled \nhigher over the point bar and large particles tended to collect in the riffle. These results indicate that \nsediment-routing is a dominant mechanism behind the formation and maintenance of riffles in \nmeandering rivers. Future work to quantify these processes will increase the effectiveness and longevity \nof river remediation design through targeted sediment augmentation instead of bedform reconstruction.
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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 | 0.000 | 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 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".