Characterizing Ice Cover Formation during Freeze-Up on the Regulated Upper Nelson River, Manitoba
Bibliographic record
Abstract
Flow control programs on regulated rivers can improve winter flow conveyance for hydropower operations. On the Upper Nelson River, station flows at Jenpeg Generating Station are reduced during freeze-up to promote formation of a smooth ice cover in often turbulent upstream areas. This ice cover reduces the risk of frazil generation, which could otherwise result in blockages and subsequent energy losses. In this study, a characterization of freeze-up conditions for the Upper Nelson River is presented through 15 years of historical observations, supplemented by a short-term detailed monitoring program (2015–2018). Observations of rapid leading edge celerity are associated with increased ice production under dampened hydraulic conditions. Analysis of ice cores and drone footage highlights the role of skim ice runs in early cover formation, while predictions of skim ice formation show agreement with ice floe taxonomy from the literature. Establishing a baseline of freeze-up conditions for the region will assist in the development of predictive tools, such as numerical models, to optimize flow control decisions for this significant hydropower system.
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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 | 0.001 | 0.001 |
| 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".