Numerical Modeling of River Ice Processes on the Lower Nelson River
Bibliographic record
Abstract
Water resource infrastructure in cold regions of the world can be significantly impacted by the existence of river ice. Major engineering concerns related to river ice include ice jam flooding, the design and operation of hydropower facilities and other hydraulic structures, water supplies, as well as ecological, environmental, and morphological effects. The use of numerical simulation models has been identified as one of the most efficient means by which river ice processes can be studied and the effects of river ice be evaluated. The continued advancement of these simulation models will help to develop new theories and evaluate potential mitigation alternatives for these ice issues. In this thesis, a literature review of existing river ice numerical models, of anchor ice formation and modeling studies, and of aufeis formation and modeling studies is conducted. A high level summary of the two-dimensional CRISSP numerical model is presented as well as the developed freeze-up model with a focus specifically on the anchor ice and aufeis growth processes. This model includes development in the detailed heat transfer calculations, an improved surface ice mass exchange model which includes the rapids entrainment process, and an improved dry bed treatment model along with the expanded anchor ice and aufeis growth model. The developed sub-models are tested in an ideal channel setting as somewhat of a model confirmation. A case study of significant anchor ice and aufeis growth on the Nelson River in northern Manitoba, Canada, will be the primary field test case for the anchor ice and aufeis model. A second case study on the same river will be used to evaluate the surface ice components of the model in a field setting. The results from these cases studies will be used to highlight the capabilities and deficiencies in the numerical model and to identify areas of further research and model development.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".