Investigation of the ice dam at Sundance Rapids on the Lower Nelson River
Bibliographic record
Abstract
Hydropower production is significantly reduced during the winter months at the Limestone Generating Station, located on the Lower Nelson River in northern Manitoba, due to an ice dam that forms 3.25 kilometers downstream at Sundance Rapids. This research investigates the annual ice dam at Sundance Rapids, focusing on identifying trends between the severity and timing of ice dam formation and release with regards to meteorological and hydrologic conditions. The absence of site-specific data in previous studies has limited progress on modelling the ice dam and exploring ice mitigation strategies to optimize power generation. The ice dam has been monitored over the 2019/2020 winter season through the establishment of a comprehensive field monitoring program, which included a weather station, trail cameras, water level loggers and water temperature sensors. Where traditional site surveys are considered too dangerous, photogrammetry and large-scale particle image velocimetry techniques were tested as a method for collecting quantitative measurements of the physical ice dam and estimating water velocities near the ice dam. The monitoring identified a strong thermal response of the ice dam, where decay/release periods generally occurred when air temperatures increased above -15°C, often coinciding with large changes in air temperature (ΔT ~20°C). As a continuation of previous work, a CRISSP2D model has been evaluated using the 2019/2020 field data. Deficiencies were found in the model’s ability to simulate anchor ice release events, and further analysis identified shortfalls in the energy budget calculations. Simulation results provided insight to the importance of a moving zero-degree isotherm in the prediction of ice dam decay/release events. The initial findings of this research provide a basis for future work to improve numerical modelling capabilities of pertinent ice processes and pursue short- and long-term ice mitigation strategies.
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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 | 0.001 | 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) | 0.001 | 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".