Advances in Feature Identification Using Tri-Axial MFL Sensor Technology
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
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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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