Guidance on configuring volumetric targets for AUT using CIVA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main standards relating to AUT (Automated Ultrasonic Testing) using zonal discrimination have long had requirements to incorporate separate channels dedicated to detecting volumetric flaws. However, none of the standards specify how the beams are to be configured. The instructions are quite generic indicating that the channels are to ensure the complete volumetric examination of the weld through-thickness. Scattering flat bottom hole targets in a calibration block such that they evenly distribute in the volume is not a guarantee that the target placement provides the required complete volume coverage. This paper illustrates how the Coverage component of the CIVA AUT module helps to identify the best placement of volumetric targets in an AUT Zonal Discrimination procedure and prevents target redundancy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it