Statistical modeling of ice cover impact on flow conveyance in the Nelson River West Channel
Bibliographic record
Abstract
For cold regions, an ice cover reduces channel conveyance and hydroelectricity generation potential. Therefore, predicting the impact of ice cover on a river-reservoir system is of critical importance for hydro producers. Ice impact can be described using historical records, where typical conditions are characterized by a daily median ice factor (IF) curve. The daily median IF curve works well only for past years with typical climatic conditions. Moreover, the median curve would not respond to climate-induced changes in the ice cover. In this research, a novel statistical (ST) model, named ST-IF, is developed to simulate the impact of river ice on the conveyance of the Nelson River West Channel (NRWC) as a function of daily air temperature. ST-IF uses a series of statistically based functions, including regression and threshold functions to estimate different characteristics of IF, such as its initial and peak values, and its daily distribution during ice-on period. Model performance was evaluated against historical records and the daily median value of the ice cover impact. Results showed that ST-IF significantly improved the simulation of each year-specific IF curve in NRWC compared to the daily median curve. Moreover, the model was used to predict the impact of ice cover under future climate conditions using 19 climate simulations. Results showed that, due to the predicted warmer future, ice cover is expected to take longer to fully form. This leads to longer Ice Stabilization Program duration, higher program implementation cost, and potential additional downstream stakeholder impacts. In addition, earlier ice impact peak date, shorter ice impact duration, and lower ice impact magnitude leading to overall higher winter hydroelectricity generation potential for Manitoba Hydro are expected in the future. Such future alterations intensify from near to far future time periods.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".