A Study of the Potential Application of Small Modular Reactors (SMRs) for Electricity Generation in the Northwest Territories
Bibliographic record
Abstract
Canada’s Northern territories face numerous challenges in the development and generation of energy. The harsh Northern climate, geographically dispersed population, and lack of electrical grids have contributed to a unique pattern of energy use in the North which is notably different than the rest of Canada. This unique environment has resulted in electricity costs that are approximately ten times higher than that of the Canadian average. The North is highly dependent on imported oil for the majority of its electricity generation. This in turn has led to a staggering level of greenhouse gas emissions or GHG which has had a serious impact on the Northern climate, with Northern temperatures becoming warmer at a rate five times that of the global average. The staggering level of GHG emissions, the high cost of electricity as well as the accelerating warming trends in the Northern climate serve as evidence for a dire need of change in policy. A key to the long term development of reliable and sustainable supply of energy in the North is the re-evaluation of old and less efficient current methods of power generation, while investigating the advantages of newer and more efficient technologies. This leads to the consideration of the public policy question of alternative methods of electricity generation such as the utilization of Small Modular Reactors (SMRs). The characteristics of the modular design of SMRs demonstrate the feasibility of utilization of SMRs to meet electricity needs in the North given the hindering geography and climate of the region. The modular concept of SMRs allow for greater simplicity in design, shorter construction periods, and a smaller plant footprint while emitting zero GHG emissions. The modular design of SMRs also incorporates operational flexibility which permits local grids to be built in a capacity which matches local electricity demand. While, there are significant benefits to the utilization of SMRs for electricity generation, potential challenges must also be recognized. These challenges include public fear of nuclear energy, licensability of SMRs, and the lack of skilled human resources in the North, among others.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".