Application of a Graded Approach to Support the National Research Universal Reactor U-2 Experimental Loop Return to Service
Bibliographic record
Abstract
Abstract The National Research Universal (NRU) reactor at Canadian Nuclear Laboratories (CNL) operated safely for over 60 years and supported a wide range of applications including, testing of fuels and materials under typical power reactor conditions in two experimental loops (U-1 and U-2). Both experimental loops had been taken out of service to address seismic deficiencies. CNL applied a graded approach to successfully return one of these loops, the U-2 Loop to service. The graded approach, without compromising safety, applied a risk informed methodology commensurate to the potential risk posed by the operation of the U-2 Loop. The work enabled the U-2 Loop to resume operation until the NRU reactor was permanently shut down in Mar. 31, 2018, generating valuable data that will be used in the development of advanced nuclear fuels and materials. This paper describes the graded approach employed by CNL that supported U-2 loop return to service (RTS). The use of graded approach is articulated to support development of safety and licensing cases for small modular reactor projects.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".