Experience, testing and future development of an ultrasonic inspection analysis defect decision support tool for CANDU reactors
Bibliographic record
Abstract
Ultrasonic inspections are undertaken on a routine basis during outages of CANDU reactors. Expert interpretation of the data generated during these inspections is used to confirm the integrity of zirconium alloy pressure tubes which house fuel bundles. The inspections form an essential component of the outage programme for CANDU reactors and as such are part of the critical outage path. Analysis of the large volume of data generated is undertaken by a small team of engineers who apply expert knowledge and interpretation through a robust review process that involves multiple human experts reviewing datasets before a consolidated view is determined. Recent work on automated defect detection and analysis on historical ultrasonic data has demonstrated the viability of a decision support tool which can aid the existing analysis process. Testing of the prototype system with engineers and domain experts has enabled improvement in accuracy and capabilities while highlighting tasks within existing procedures where explicit descriptions of all scenarios cannot feasibly be specified and where therefore human judgement plays a more important role. Development of the software, as a support tool to enable more rapid analysis of typical defects, is intended to enable increases in the time available for experts to consider more complex defects. This paper discusses the development of the software prototype, potential deployment routes for the prototype system, considering the interactions with the existing process, benefits which may be realised and limitations that may be addressed through the use of data-driven analysis techniques
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".