Lessons Learned in Planning the Canadian Nuclear Legacy Liabilities Program
Bibliographic record
Abstract
In 2006, Atomic Energy of Canada Limited (AECL) and Natural Resources Canada (NRCan) began implementing a $7B CDN, 70-year Nuclear Legacy Liabilities Program (NLLP) to deal with legacy decommissioning and environmental issues at AECL nuclear sites. The objective of the NLLP is to safely and cost-effectively reduce the nuclear legacy liabilities and associated risks based on sound waste management and environmental principles in the best interest of Canadians. The liabilities include shutdown research and prototype power reactors, fuel handling facilities, radiochemical laboratories, support buildings, radioactive waste storage facilities, and contaminated lands at several sites located across eastern Canada from Quebec to Manitoba. The largest site, Chalk River Laboratories (CRL) in Ontario, will continue as an operational nuclear site for the foreseeable future. Planning and delivery of the Program is managed by the Liability Management Unit (LMU), a group that was formed within AECL for the purpose. The composition and progress of the NLLP has been reported in recent conferences [1, 2]. The NLLP comprises a number of interlinked decommissioning, waste management and environmental restoration activities that are being executed at different sites, and by various technical groups as suppliers to the LMU. Many lessons about planning and executing such a large, diverse Program have been learned in planning the initial five-year “start-up” phase (which will conclude 2011 March), in planning the five-year second phase (which is currently being finalized), and in planning individual and interacting activities within the Program. The activities to be undertaken in the start-up phase were planned by a small group of AECL technical experts using the currently available information on the liabilities. Progress in executing the Program was slower than anticipated due to less than ideal alignment between some planned technical solutions and the actual requirements, as well as the limited available resources of the suppliers to execute the work. Several internal and external reviews of the Program during the start-up phase examined progress and identified several improvements to planning. These improvements included strengthening communications among the groups within the Program, conducting more detailed advance planning of the interlinked activities, and being cautious about making detailed commitments for activities for which major decisions had yet to be made. The second phase was planned using a dedicated core team, and involved much more involvement of the suppliers to ensure feasibility of the proposed program of work and more detailed specification of the required resources. Priorities for executing the diverse activities in the Program were originally set using criteria based on the risks that the liabilities presented to health and safety, to the environment and to AECL’s ability to meet its obligations as the owner-operator of licensed nuclear sites. The LMU later recognized that the decision criteria should also explicitly include the value gained in reducing the risks and liabilities for expended funds. Greater consideration should be given to mitigating risks to the execution of the Program that might materialize. In addition, licensing strategies and processes should be better-defined, and waste characterization methods and disposition pathways would have to be put in place, or clearly identified, to deal with the wastes the Program would generate before many of the planned activities could be initiated. The NLLP has developed several processes to assist in the detailed planning of the numerous projects and activities. These include developing a more formal procedure for setting priorities of the different parts of the Program, preparing an Integrated Waste Plan to identify the optimal suite of support facilities to be constructed, the creation of a series of “pre-project initiation” procedures and documents to guide the development of well-founded projects, and the use of staged decision-making to incorporate more flexibility to adjust Program strategy and the details of implementation at planned decision points. Several Case Studies are outlined to illustrate examples of the application of these planning techniques.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".