Multi-MFL Measurement Assessment using Gaussian Mixture Model
Bibliographic record
Abstract
Magnetic flux leakage (MFL) is the most popular in-line inspection (ILI) technique to inspect pipeline corrosion. However, the random measurement error of MFL cannot be negligible due to measurement variations. Since the ILI is usually conducted with two different types of MFL devices, data from two MFL systems are compared to assess the measurement result in this paper. Because the difference between two measurements consists of both random and systemic errors associated with individual MFL system, measurement conversion is conducted first to eliminate the influence of the systemic error. In this paper, a Gaussian probability density function is employed to represent the measurement data because of the randomness of measurement error. Then, the process to eliminate the systemic error between two systems becomes the alignment of two Gaussian mixtures. Besides, the L2 distance is employed as the distance measure to align Gaussian mixtures and assess the measurement confidence. The validity of proposed method is proven in the numerical example.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".