Investigation and Assessment of Low-Frequency ERW Seam Imperfections by EMAT and CMFL ILI
Bibliographic record
Abstract
The occurrence of low-frequency Electric Resistance Welded (LF-ERW) or Electric Flash Welded (EFW) line pipe imperfections has been the root cause of many integrity management initiatives to minimize and mitigate the risk of pipeline failure across the oil & gas pipeline industry. Since their first appearance in the 1920s, defects in or near the LF-ERW and EFW seam repeatedly lead to either hydrostatic test or in-service failures. Where in the past In-Line Inspection (ILI) technologies might have experienced limitations in addressing vintage ERW line pipe defects, modern smart ILI technologies show enhanced capabilities. High resolution Electro-Magnetic Acoustic Transducer (EMAT) and Circumferential Magnetic Flux Leakage (CMFL) ILI technologies have advanced in the recent years enabling more challenging inspections. This paper summarizes the inspection results of 22″ ERW line pipe defects detected and reported by EMAT and CMFL. Correlation of ILI and manual NDE data enables evaluation of current ILI capabilities and improvement of current defect assessment methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".