Addressing the Challenge of Inspecting Buried Nuclear Piping in Nuclear Power Plants
Bibliographic record
Abstract
Similar to many other industries, nuclear power plants (NPPs) have many kilometres of buried pipe that is not readily accessible for direct inspection. Given the nature of the systems, the nuclear industry experiences additional challenges as many services run in the same area, leading to what is described as a “spaghetti bowl” of piping. As a result, the traditional indirect, over the line, inspection practices developed for the oil and gas industry have not always been successfully applied at nuclear power plants. To address this issue, a collaborative Electric Power Research Institute/CANDU Owners Group (EPRI/COG) research project was established with Mears Group Inc. and Atomic Energy of Canada Limited (AECL) Nuclear Laboratories. In this program, initial testing of four methods was carried out at the Mears Group Inc, test site that had been modified with additional grounding systems to simulate a nuclear power plant. This was followed by testing of the same methods at the AECL Nuclear Laboratories Chalk River site. This paper will discuss the results of those studies and present some of the findings that were made that can help to overcome the challenges faced by Nuclear Power Plants.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".