Smart Pigs and Defect Assessment Codes: Completing the Circle
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
Abstract Smart pigs are used extensively as part of integrity management plans for oil and gas pipelines to detect metal loss defects, with magnetic flux leakage (MFL) technology being the most-widely used. The MFL signal gives an inferred defect size, not a direct measurement: when the signal is translated into a defect size, it has associated sizing tolerances and confidence levels. The complexity of signal analysis means that these sizing tolerances and confidence levels are difficult to determine and apply in practice. They have a major effect when assessing the significance of the defect, and when calculating corrosion growth rates from the results of multiple inspections over time. This paper describes how sizing algorithms are constructed and how the quoted tolerances are derived. Probability theory can be used to estimate the likelihood that a defect is smaller or deeper than the reported value. Finally, the effect of defect sizing tolerances and their confidence levels on corrosion growth projections is illustrated, and how they must be taken into account in any defect assessment is emphasised.
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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 | 0.000 | 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 it