An experimental study of bedload transport in partially ice-covered channels
Bibliographic record
Abstract
The onset of winter in cold climates often leads to the formation of border ice along riverbanks, a phenomenon that can persist for a significant portion of the winter season, thereby affecting river channels' dynamics and geomorphological structure. Understanding the effects of ice cover on sediment transport and bed morphology is crucial, as these elements significantly influence flow resistance and river behavior. While there is extensive literature on sediment transport and bed morphology in open channel flow and several studies in conditions of complete ice cover, research in partially ice-covered channels is sparse.\nTo address this gap, the current study conducted laboratory experiments at the Hydraulics Research and Testing Facility at the University of Manitoba, Canada. The research aimed to explore the effects of border ice, including the extent of ice cover, variations in flow strength, and the asymmetry of border ice on bedload transport rate and distribution, as well as bed morphology and bedform characteristics. Various experimental setups, ranging from open channel flows to symmetric and asymmetric border ice and fully ice-covered conditions, were analyzed to assess bedform dimensions and bedload transport rates against theoretical models in the literature. This comparison confirmed the reliability of the models in describing bedform features under varying ice conditions. Moreover, despite noticeable differences in bedload transport rates across the channel width, in partially ice-covered conditions, the cross-section-averaged bedload transport rate could still be accurately estimated using conventional equations adapted for open channel and fully ice-covered flows by adjusting for the additional boundary created by the ice in the calculation of the wetted perimeter.\nThe findings also demonstrate that the presence, positioning, and varying extents of partial ice cover significantly influence bedforms within the channel and the distribution of bedload transport. Notably, the impact of ice coverage is more pronounced at lower flow strengths and diminishes as flow strength increases. This research provides essential insights into the complex interactions between ice cover and sediment transport.
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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) | 0.002 | 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".