Bibliographic record
Abstract
Atomic Energy of Canada Limited (AECL) has established a successful, internationally recognized line of CANDU® reactors that use heavy water moderator and pressure tubes; in particular the medium-sized CANDU-6 reactor. AECL has consistently adopted an evolutionary approach to the enhancement of CANDU nuclear power plant designs over the last 30 years. This approach has been extended further in the development of the ACR-1000®. The ACR-1000 design has evolved from AECL’s in-depth knowledge of CANDU structures, systems, components and materials, as well as the experience and feedback received from builders, owners and operators of CANDU plants. While retaining the proven strengths and features of CANDU reactors, the ACR design incorporates innovations and state-of-the-art technologies where appropriate. Part of this innovation is to develop an efficient and optimized plant layout that includes diverse features and requirements from many engineering disciplines. The ACR-1000 plant layout satisfies safety requirements and regulations and includes input from construction, operations and maintenance feedback from existing stations/utilities, while maintaining the process functional configuration for the overall layout of the plant, building and systems. This paper discusses a number of key aspects in developing the ACR-1000 plant layout design using representative sketches and 3D CADDS illustrations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.012 |
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".