CIVA Modelling Module for Zonal Discrimination Method Part 1-Calibration Block
Bibliographic record
Abstract
The 2023 edition of CIVA simulation software has incorporated a module specifically designed for pipeline production weld inspections (Automated Ultrasonic Testing or AUT). Both Zonal Discrimination Method (ZDM) and Total Focussing Method (TFM) options have been included. Unlike the standard ultrasonic module, the “AUT” module has provision to run and display the outputs from multiple channels. This allows for the echo-dynamic display to be seen in a view similar to the strip-chart display commonly used with the zonal discrimination method. Having configured the delay laws to generate an acceptable calibration, CIVA tools such as the meta-model and POD modules can then be used to assess the reliability of the setup (including the efficacy of the calibration block design) for a qualification process. This paper illustrates how the calibration block design is executed for the zonal discrimination method. Results are compared to data collected for a field qualification. A subsequent paper is planned to compare the statistical analysis carried out in the field to assess the inspection reliability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.019 |
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".