Bibliographic record
Abstract
Of these various factors that influence the Magnetic Flux Leakage (MFL) signal, stress is the most complex and the least understood. Pipe wall operating stresses may exceed 70% of the yield strength, but much higher local stress levels are present around defects because of stress concentrations. Understanding how these stresses affect MFL signals is crucial to accurate defect depth predictions. We have conducted a number of studies that investigate the effects of bulk and local stresses on magnetic behaviour and MFL signals. Both single defects and interacting (2-hole) defects have been investigated. In addition to MFL studies, we have used a Magnetic Barkhausen Noise (MBN) measurement technique to characterize magnetic behaviour of the sample in response both to local and bulk stresses. In general we find that, for typical inspection geometry (axial magnetizing field and hoop stress) the MFL signal decreases as stress increases. At high flux densities stress effects diminish, but are still significant. Although the general trends described above are common to all defects, different types of defects (through wall hole, blind hole, interacting defects) all display uniquely different MFL stress responses. We attribute this to the difference in local stress distributions (stress concentrations) around these defects.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".