Sediment Transport Beneath a Simulated Partial Ice Cover: Effects of Asymmetric Border Ice
Bibliographic record
Abstract
With the onset of winter in cold regions, border ice begins to form, impacting sediment transport rate and distribution. Understanding the effect of ice cover is crucial in regions with prolonged sub-freezing temperatures, as water bodies remain frozen for a considerable part of the year. While the existing literature includes many studies on sediment transport in open channel flow and several studies on completely ice-covered flow, there is limited research on sediment transport in partially ice-covered channels. Addressing this gap, the current study conducted laboratory experiments in a rectangular flume at the Hydraulics Research and Testing Facility, University of Manitoba, Canada. The investigation focused on examining the influence of asymmetric border ice, varying coverage ratios in asymmetric partially ice-covered flow, and changing flow strengths on bedload transport rate and distribution. Additionally, a comparison was made with symmetric partially ice-covered flow conditions. The findings indicate that the presence of asymmetric border ice indeed affected the bedload transport distribution within the channel, causing non-uniform bedload transport distribution across the channel width, with peak values concentrated in the center of the open flow section. Increased asymmetry in border ice leads to greater asymmetry in bedload transport rate distribution. Despite these pronounced differences in bedload transport rate across the channel width, the cross-section-averaged bedload transport rate could still be estimated utilizing the conventional equations, used for open channel flow, fully ice-covered flow, and symmetric partially ice-covered flow, with the effect of ice cover accounted for by incorporating an additional boundary in the calculation of the wetted perimeter, leading to adjustments in the flow strength.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".