Quantitative interpretation of multifrequency eddy current data by using data fusion approaches
Bibliographic record
Abstract
Multi-frequency techniques are widely adopted for eddy current testing. One of the advantages of these techniques can be deduced from the skin depth formula (formula available in paper) where delta is the standard depth of penetration at excitation frequency f, with the other two parameters, mu and sigma, related to material properties. Thus, an inspection can be performed at several depths into the material with the simultaneous use of multiple frequencies. To investigate the potential of a multi-frequency eddy current technique (MFECT) for corrosion quantification, an experiment was carried out on a two-layered fuselage lap joint splice. Two data fusion approaches, namely Bayesian inference and multiresolution analysis, are investigated in this study to fuse eddy current images of different frequencies. The corrosion types are classified based on the percentage of material loss. The estimated thickness results, based on the fusion processes, are compared with accurate thickness maps obtained from teardown X-ray inspection data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".