Parameterizing Magnetic Flux Leakage Data for Pipeline Corrosion Defect Retrieval
Bibliographic record
Abstract
Magnetic flux leakage (MFL) is the most popular in-line inspection (ILI) technique to inspect pipeline corrosion. The collected MFL signals are characterized to estimate the profile of corrosion defects. However, the estimation error could be huge for certain corrosion areas because of the signal interference between adjacent defects. To retrieve these corrosion areas from the whole pipeline, one accurate and reliable representation of the corrosion defect is critical while no relevant research has been done yet. In this study, the concept of MFL data parameterization is proposed first. Parameterization is a contextual defect representation, which considers both corrosion defect and its surroundings to deal with the signal interference. Besides, one two-dimensional Gaussian function is introduced to denote the interference strength, and three parameterization models are then developed to obtain a reliable representation of corrosion defect. In the end, two experiments on corrosion defect retrieval are conducted to evaluate the performance of three parameterization models.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".