Bibliographic record
Abstract
To meet the growing demand for electricity in Ontario, the federal and the provincial governments are expanding nuclear power generation in Ontario. Part of these expansion plans include developing North America’s first small modular reactor (SMR) at the Darlington New Nuclear Project site (Small modular reactors: Darlington SMR 2024). While cheaper and easier to build than traditional reactors, researchers at Stanford University and the University of British Columbia have found that SMRs produce more nuclear waste per unit of energy compared to traditional reactors as a result of increased neutron leakage (Liou, 2023 ; Krall et al., 2022). Nuclear power is a large step towards achieving net-zero but this expansion makes finding more storage for the nuclear waste produced increasingly necessary (2030 Emissions Reduction Plan – Sector-by-sector overview 2024). This project assesses appropriate sites in Ontario to establish a new nuclear waste disposal site. A GIS analysis will be predominantly relied upon to create a model to evaluate various layers and site characteristics. Factors currently being assessed in the model are groundwater, potential disturbances to the land (ie. current or future mining activity), and whether the lands are federally or provincially protected lands. The Province is split into 71 districts, in accordance with the Ecosystems of Ontario Ecodistricts. These districts are then either included or excluded in the model based on the development of their aquifers and whether the land contains any natural resources that are currently, or could be, mined. The remaining Ecodistricts are then overlain with protected lands and the remaining sites are usable. Preliminary results have shown sites available are predominantly in northern Ontario. Future factors that will be incorporated into the model include a soil and geology suitability assessment and an accessibility assessment, using existing road access to the site as the criteria for this factor. ReferencesKrall, L. M., Macfarlane, A. M., & Ewing, R. C. (2022). Nuclear waste from small modular reactors. Proceedings of the National Academy of Sciences, 119(23). https://doi.org/10.1073/pnas.2111833119Liou, J. (2023, September 13). What are small modular reactors (smrs)?. IAEA. https://www.iaea.org/newscenter/news/what-are-small-modular-reactors-smrsSmall modular reactors: Darlington SMR. OPG. (2024, December 4). https://www.opg.com/projects-services/projects/nuclear/smr/darlington-smr/
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.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".