Selecting the correct electromagnetic inspection 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it