The technical, economical and environmetal feasibility of implementing Small Modular Reactors (SMRs) : A case study of Rankin Inlet, Canada
Bibliographic record
Abstract
This study examines the various challenges involved in implementing Small Modular Reactors (SMRs) in Rankin Inlet, Canada. This region has high dependence on highcost and contaminant energy sources due to its extreme climate and its isolation. The interest of this study is to replace their use of fossil fuels and to produce clean and local energy to improve citizen's quality of life through new fission technologies as SMRs. These reactors are in current development and testing its effectiveness. A combined technical, environmental, and economic evaluation was conducted to assess the suitability of SMRs for this context. The methodology included cost extrapolation from existing data, estimation of COz emission reductions, and calculation of the Levelized Cost of Electricity (LCOE) and the Return on Investment (ROI) for a 3 MWe reactor operating over a 60-year lifetime. The analysis shows that the SEALER, in terms of technical feasibility, is suitable for Arctic conditions due to its passive safety systems, compact size, and long operational life. From an environmental perspective, the replacement of diesel generators with SMR would account for a significant reduction in greenhouse gases emissions. In economic terms, while the initial investment costs are high, the long-term performance demonstrate the strong potential for cost-effectiveness and energy independence of those reactors. The study concludes that SMRs are a promising alternative to fossil fuels in remote communities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".