The Low-Level Radioactive Waste Management Office: Thirty Years of Experience in Canada - 13308
Bibliographic record
Abstract
This paper reviews thirty years of progress by the Low-Level Radioactive Waste Management Office (LLRWMO) in developing and implementing low-level radioactive waste (LLRW) remediation projects and environmentally safe co-existence strategies. It reports on the present status and the future of the national historic waste program in Canada. There are over two million cubic metres of historic LLRW in Canada. Historic LLRW is broadly defined as LLRW that was managed in the past in a manner that is no longer considered acceptable and for which the original owner cannot reasonably be held accountable. In many cases, the original owner can not be identified or no longer exists. The LLRWMO was established in 1982 as Canada's agent to carry out the responsibilities of the federal government for the management of historic LLRW. The LLRWMO is operated by Atomic Energy of Canada Limited (AECL) through a cost-recovery agreement with Natural Resources Canada (NRCan), the federal department that provides the funding and establishes national policy for radioactive waste management in Canada. The LLRWMO expertise includes project managers, environmental remediation specialists, radiation surveyors, communications staff and administrative support staff. The LLRWMO in providing all aspects of project oversight and implementation contracts additional resources supplementing coremore » staff capacity as project/program demands require. (authors)« less
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".