Geographical Considerations in Site Selection for Small Modular Reactors in Saskatchewan
Bibliographic record
Abstract
Saskatchewan is one of Canada’s highest emitters of greenhouse gases, largely due to the burning of lignite coal to generate electricity. The province is also the world’s second largest producer of uranium. This research was intended to establish a process for evaluating geographical considerations in site selection for small modular reactors (SMRs) in Saskatchewan. SMRs are the next generation of electrical power, producing less than 300 megawatts (MW) and featuring a basic design that offers enhanced safety, health, and environmental benefits compared to traditional reactors. Selecting an SMR site is a two-stage process: (i) Identifying candidate site locations based solely on available geographical, economic, and logistical data—an objective process—and (ii) refining the potential locations based on public perceptions, social conventions, and political will—a subjective process. This study focused on the objective geographical considerations in SMR site selection in Saskatchewan. The study areas were subjected to a multi-criteria decision analysis based on specific criteria drawn from various Canadian federal regulation documents. Criteria weights were assigned using the analytical hierarchy process, with results for two different types of criteria weights applied for the purpose of demonstration. Three distinct cases of criteria fuzzy standardization were conducted to assign spatial suitability values for all the criteria. Spatial decision-making models were implemented in a geographic information system to identify candidate sites. Geographical maps constructed from the findings showed suitable sites for SMRs, ranging from very suitable to unsuitable based on the geographical analysis of the study area.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".