Field monitoring and numerical modelling of the ice dam at Sundance Rapids
Bibliographic record
Abstract
Manitoba Hydro’s Limestone Generating Station, located on the Nelson River in Manitoba, experiences increased winter water levels in the station tailrace and reduced energy production potential due to a large ice dam that forms downstream at Sundance Rapids. Previous work included the development of a CRISSP2D site model to simulate the complex ice and hydraulic conditions and the establishment of a field monitoring campaign (2019/20) to collect site-specific hydraulic and meteorological data. This research expanded on previous work by performing three years of winter field monitoring (2020/21–2022/23) and assessing the CRISSP2D models’ ability to simulate on-site observations. The goal was to expand the current understanding of the ice dam that forms at Sundance Rapids to aid in the future development of mitigation techniques to reduce the impact on tailwater levels and energy production at the Limestone Generating Station. Analysis of ice dam impacts on tailrace staging confirmed that over the three winters release was driven by thermal conditions. No thermal condition consistently correlated to release, revealing that other factors influence event variability. Quantitative analysis indicated that ice dam strength impacts the magnitude of release events. Qualitative observations found that discharge may impact thermal release by transporting more/less above 0°C water over the rapids. CRISSP2D energy budget component calculations were modified to improve the comparison between simulated and observed. Following modifications, the model was able to simulate ice dam growth successfully. However, it was determined that the existing thermal release mechanism could not capture release as observed on site. Future work should focus on improving the understanding of ice dam release and the factors that impact it (i.e., discharge, ice dam strength) to assist in developing a site-specific release mechanism in CRISSP2D.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".