Methodology for Identifying Blunt Flaws Using Ultrasonic In-Service Inspection Data
Bibliographic record
Abstract
This paper presents a methodology for identifying blunt flaws in piping using in-service data from ultrasonic inspection tools. The method is applicable to data obtained from predetermined inspection grids, or directly from bracelet or array type inspection tools. The flaw edge and size are identified by calculating the three-dimensional slope vector (i.e., magnitude and direction) of each point in the kriging interpolated wall thickness profile. The transition from a steeper slope to a relatively flat profile is then used by a search algorithm to determine the flaw edge. The method is applied to the flaw assessment of feeder piping in CANDU nuclear reactors. The results show that in addition to identifying blunt flaws, the developed methodology also provides a convenient way for characterizing the flaw dimensions for structural integrity assessment. The uncertainty in the results is mainly attributed to the signal loss and coverage error associated with the inspection data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".