Management of radioactive waste from application of radioactive materials and small reactors in non-nuclear industries in Canada and the implications for their new application in the future
Bibliographic record
Abstract
A large number of artificial-origin radionuclides from irradiation in small reactors and/or nuclear reactions in accelerators are currently used in non-nuclear industries such as education, oil and gas, consumer merchandise, research, and medicine. Radioactive wastes from the use of these radionuclides in non-nuclear industries include expired sealed radioactive sources, biological materials, radionuclide-containing chemicals, contaminated equipment, and very small quantities of used nuclear fuel. Although being less challenging and complex than nuclear energy production and research waste streams, these wastes are subject to the common nuclear regulations by the Canadian Nuclear Safety Commission, and are managed following domestic and international standards and guidelines made by the Canadian Standards Association, International Atomic Energy Agency, and International Organization for Standardization. Management practices used in the nuclear industry in Canada are commonly applied to the non-nuclear industry radioactive waste streams, such as waste handling, treatment, packaging, storage, transportation, clearance and exemptions, and disposal. The half-lives of radionuclides in non‑nuclear applications range from hours to thousands of years, and their activities in non-nuclear industrial applications can be as low as their clearance level or as high as the upper limits for intermediate level radioactive waste. Waste containing only short half-life radionuclides is placed in temporary storage to allow decay, and then is cleared and disposed of through non-radioactive waste routes. Non‑clearable waste materials are treated, consolidated, and managed along with radioactive waste generated from the nuclear industries at designated radioactive waste management sites.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".