Challenges and Perspectives of Deep Geological Repositories for Nuclear Energy
Bibliographic record
Abstract
Sustainable development and environmental preservation are unattainable without addressing climate change and its severe impacts.A key component of this effort is the adoption of clean energy technologies, such as nuclear, wind, and solar energy, which are essential to achieving net zero greenhouse gas (GHG) emissions by 2050.Nuclear energy, in particular, is crucial for reaching this goal both in Canada and globally.Currently, nuclear power supplies approximately 10% of the world's electricity, 15% of Canada's, and 60% of Ontario's needs.Despite its benefits, nuclear energy generation results in the production of hazardous nuclear waste.The sustainable and safe management of this waste is paramount, and several countries around the world are investigating the use of deep geological repositories, which involve the disposal of nuclear waste in deep rock formations, as sustainable solutions for nuclear waste.This lecture will address the significant geotechnical engineering challenges associated with the design and implementation of deep geological repositories for nuclear waste.It will present the latest research advancements aimed at overcoming these technical obstacles and explore the future prospects of deep geological repositories as a sustainable solution for nuclear waste management.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".