Advances in Feature Identification Using Tri-Axial MFL Sensor Technology
Bibliographic record
Abstract
Pipeline operators have been using intelligent in-line inspection (ILI) tools as part of their pipeline integrity management systems for several decades now. A wide variety of ILI tools have been developed to serve a multitude of uses. Most notable is the detection, locating, and sizing of metal loss corrosion. Magnetic Flux Leakage Technology (MFL) was developed for that exact purpose, however over the years technology and innovation has vastly improved the capabilities of MFL tools. This paper contains a comparison of historical and current pipeline feature identification/classification capabilities for axial magnetizing MFL tools with Tri-Axial sensor technology. The pipeline features discussed include corrosion, mechanical defects, structural pipeline components, as well as the physical and magnetic parameters that affect accurate identification, location, and/or sizing. Some of these features have never been detected, identified, or reported in the past, and now constitute a significant portion of the training and testing procedure that occurs in the certification of a new MFL data analyst.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".