Constructability -- from Qinshan to the ACR
Bibliographic record
Abstract
Atomic Energy of Canada Limited (AECL) has recognized the importance of constructability for many years, and it is applying its principals to CANDU projects with increasing success. The CANDU 6 Nuclear Power Plant has been constructed eleven times in the last 25 years. However, the last two units completed on the Qinshan project in China have employed some very innovative construction methods that have not been used on the previous units. In order to make nuclear power generation more competitive, shorter construction schedules and reduced project cost and risks are essential objectives. The application of constructability principles is a major contributor to achieving these objectives. The success of Qinshan has increased the confidence in the new construction methods, which are being implemented on the ACR (Advanced CANDU Reactor) successfully. An ACR construction strategy that utilizes advanced construction techniques has been developed by AECL. The strategy includes paralleling of activities by using extensive modularization and the vertical installation of equipment and modules into the reactor building using a VHL (Very Heavy Lift) crane. This strategy allows short schedules to be met with a minimum risk to the project.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".