Bibliographic record
Abstract
Canada is one of many countries around the world that use nuclear reactors to generate electrical power, in part to reduce our carbon footprint. Yet this energy produces hazardous, long-lived waste that emits dangerous radioactivity for tens of thousands of years. Nuclear waste, stored temporarily for decades, must be safely disposed of so it will not pose a serious threat to human health and the environment. This means placing it in locations deep underground in granite, sedimentary rock, or clay. Canada’s ideal location is somewhere on the Canadian Shield, the 2.5-billion-year-old crystalline rock that undergirds much of the country. Beginning in 2010 some twenty-two communities, most in Ontario, volunteered to host the repository. In Deep Disposal William Leiss explains the challenges that have arisen in the evaluation of potential sites over the last decade. High-level nuclear waste is the most hazardous byproduct of an energy source that is incredibly useful and increasingly in demand. Finding the ideal place to store it permanently is an urgent policy crisis facing our country. Deep Disposal reveals the nature of this crisis and how we might overcome it.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.160 | 0.054 |
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".