PR652-203801-R02 Magnetic Object Model for Large Standoff Magnetometry Measurement
Bibliographic record
Abstract
Ferromagnetic pipeline steel may exhibit magnetization, even in the absence of applied magnetic fields, due to remnant fields or the presence of pipe wall stress. Remnant magnetization may be present from previous or existing exposure to a magnetic field, while pipe wall stress induced magnetization can result from line pressure, environmental stresses due to settling or geohazard conditions, and residual stresses due to nonuniform plastic deformation caused by manufacturing processes, installation or operating conditions. The local stress state of the pipeline may also be altered by corrosion or damage. The physical basis for magnetization in pipelines due to intrinsic and resident stresses is examined here using the magnetic object (MO) model. MOs are characterized as regions of relatively independent magnetic behaviour, typically about the size of a ferromagnetic steel grain, to which expressions for the magnetic energy of local domain structures can be applied. The lowest energy state for an MO is a flux-closed structure, but the presence of stress can modify the MO energy through inverse magnetostrictive effects on the domain structure and thereby, produce a state of magnetization. This magnetization may be altered by the introduction of additional stress sources including pressurization of the pipe, geological-environment effects, sources of magnetization that include the proximity of other ferromagnetic pipes, even those comprising sections of the same pipeline, and changes in the pipe structure that may be brought about by deformation, corrosion or cracking. This work shows that the fundamental building block of the MO, combined with considerations of overall changes in domain structure due to these factors, can be used to model the generation of magnetic fields measured outside of pipeline structures. This will have implications for understanding sources of pipeline magnetization that are passively measured above buried oil and gas pipelines with the objective of detecting anomalous conditions that may indicate compromised conditions for safe pipeline operation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".