COST EVALUATION OF PROPOSED DECOMMISSIONING PLAN OF CANDU REACTOR
Bibliographic record
Abstract
Nuclear decommissioning is the final technical and administrative process in the life cycle of nuclear power operation. Experience over the last decade has demonstrated that in general, the process of decommissioning and its cost evaluation has reached industrial maturity, although specific techniques continue to evolve. Owners and licensees of nuclear power plants are generally responsible for developing cost estimates of decommissioning, and a good understanding of these costs is fundamental for the development of estimates based on realistic decommissioning plans. The use of these techniques in the cost evaluation of the decommissioning of nuclear facilities continues to increase this experience. This research has been carried out keeping in mind to evaluate an economical and feasible cost for the proposed decommissioning plan of CANDU (Canada Deuterium Uranium) reactor. Work is done in the major areas of cost estimations for DECON (immediate dismantling), SAFESTOR (deferred dismantling) and ENTOMB (on site end-state). These alternatives were analysed and SAFSTOR method was recommended for 40+ years old, Canadian designed, first generation CANDU reactor decommissioning. This paper provides a cost estimation for a decommissioning as recommended in the analysis performed. A cost of 200 Million $ is evaluated for SAFSTOR decommissioning alternative of proposed CANDU reactor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".