1 IAEA-CN-123/01/O/02 NUCLEAR KNOWLEDGE MANAGEMENT STRATEGIES IN CANADA
Bibliographic record
Abstract
Abstract. An effective knowledge management strategy must encompass three basic elements; a sound resource management and training strategy to maintain nuclear competency in the face of accelerated retirements of current generation of experts and the development of advanced products, effective engineering tools to preserve the current technology and design basis and effective information management systems to facilitate pooling and sharing of information amongst different entities. The Canadian Nuclear Industry and its regulatory agency, the Canadian Nuclear Safety Commission (CNSC) recognized the importance of nuclear knowledge management and have already implemented a number of initiatives, in order to maintain competency, capture and preserve existing knowledge, advance the nuclear technology, develop future nuclear workers and maintain a critical R&D capability. The paper describes activities and initiatives undertaken or in progress in Canada in order to ensure a smooth transition of nuclear knowledge to the next generation of nuclear workers. Although this paper intends to address the Canadian scene in general, special emphasis will be placed on activities currently underway at Atomic Energy of Canada Limited (AECL) as the design authority and guardian of the CANDU technology. 1.
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 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".