Geographical Considerations in Site Selection for Small Modular Reactors in Saskatchewan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it