Investigation and modelling of ice processes in the Nelson River’s Outlet Lakes Area
Bibliographic record
Abstract
Ice is a prominent characteristic of water bodies in cold regions. For rivers regulated for hydropower operations, the production of ice particles can result in obstructions and subsequent performance issues during energy production. Rough and thickened ice covers resulting from high flow conditions can also lead to substantial hydraulic losses. While ice formations impact hydropower operations, a river’s flow hydrograph also influences ice processes from freeze-up through break-up. Research investigations into the influence of regulation on ice processes benefits not only hydropower practioners, but also those who are impacted by hydropower operations. Further, understanding these cause-and-affect relationships supports design of innovative tools to quantify the impact of ice on river hydraulics. In this study, a detailed characterization of ice processes is presented for the regulated Upper Nelson River region located at the outlet of Lake Winnipeg in Northern Manitoba, Canada. With a focus on freeze-up and mid-winter processes, this characterization informed design of a 2D numerical modelling methodology to simulate ice-affected winter hydraulics. Model development included simulation of both thermal and dynamic ice phenomenon, which relied on derivation of numerous site-specific hydraulic functions. The presence of significant skim ice runs in this region inspired development of a novel treatment to simulate freeze-up jamming of skim ice floes on very mild-sloped rivers. The modelling methodology shows strong performance in simulating both freeze-up and mid-winter hydraulics, which is a signficiant contribution considering the complexity of this lake-outlet system. A quantitative evaluation of the effects of climate change on river ice hydraulics is included, with future projection of shorter and warmer winters leading to greater cumulative discharge from Lake Winnipeg. While discharge increases may lead to increased power production in future years, concurrent projections of increased inter-annual variability may present new operational challenges. Findings from this original research can be applied not only to the Nelson River, but also other regulated regions that are impacted by river ice.
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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 | 0.001 | 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".