Modelling river ice processes in a small-steep-regulated river
Bibliographic record
Abstract
River ice processes in small-steep-regulated rivers are distinct from those in larger and low-gradient rivers due to fast flow, shallow depths, highly variable and often unstable flow conditions. Existing river ice models are not well suited for these rivers because they rely on assumptions that misrepresent their characteristics and do not account for key ice processes, such as aufeis evolution. In this study, the University of Alberta’s River1D model was revised to improve its ability to simulate ice processes in small-steep-regulated rivers. Several key enhancements were implemented, including removal of the floating border ice assumption, a border ice retreat algorithm, an aufeis formation algorithm, a quasi-2D border ice profile algorithm, and refinement of anchor ice thermal release algorithm. The revised model was calibrated and validated on the Aishihik River, a regulated river in the Yukon, Canada, using comprehensive field data collected over two winter seasons. Water temperature and water level simulations achieved RMSEs of 0.11 °C and 0.20–0.25 m, respectively. Modelled border ice growth and retreat patterns aligned with field observations. Floodplain aufeis processes were reasonably captured, with differences between simulated and observed thicknesses ranging from 0.01 to 0.23 m, and the simulated extent in good agreement with field measurements. The enhanced River1D model offers a valuable tool for assessing the impacts of flow regulation, mitigating ice-related hazards, and evaluating climate change scenarios in small-steep-regulated rivers.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".