Selecting the correct electromagnetic inspection technology
Bibliographic record
Abstract
Eddy current (EC) technology for inspection of conducting materials is a potential solution when conditions preclude the application of other methods. Such conditions include presence of sound absorbing coatings, unavailability of a couplant, multiple conducting layers with air gaps, limited access or near surface cladding. However, the choice of a particular EC technology may not be clear due to sources of electromagnetic interference, choice of probe design, target configuration or even available equipment. In addition, the choice of EC based technologies is extensive, including conventional EC, low frequency EC, remote field EC and pulsed EC. Each of these technologies has its own challenges and limitations, which need to be considered prior to a commitment to system development. Probe choice becomes a function of the particular technique that has been selected and may include ferrite core sensing coils, GMRs or eddy current coil array. Finally, EC signal analysis methods need to be selected based on effects of potentially multiple varying parameters. This paper examines the potential of electromagnetic inspection technology, discussing its limitations, effects of common essential parameters and analysis methodologies. Examples of recent technology applications are given and the benefits and limitations of various technologies are compared and discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".