PR652-203801-R01 Large Standoff Magnetometry (LSM) Technology Literature Review
Bibliographic record
Abstract
With improving accuracy and sensitivity of magnetic sensors, an attractive branch of the magnetic memory method (MMM) technique has been developed. Large Standoff Magnetometry (LSM) is an emerging non-destructive, remote, passive, non-contact and magnetic test method based on Villari effect, which could be applied to detect anomalies related to elevated stresses. The robust technology searches for Stress Concentration Zones (SCZs) in steel pipelines and ferromagnetic structures. These hot spots are mainly associated with corrosion, crack and mechanical damages such as dent and bucket, or landslide, which could be as a result of geohazards or external loadings. The technique has become very attractive because of its operational simplicity and economic advantages especially in hard-to-pig pipelines. The LSM Technology has potential to be deployed as a screening tool to identify areas that require further examination, to improve target inspections, and prioritize excavations. At its current state of development, the technique could be considered as a complementary technique in integrity assessment technology. The applications of LSM technology include ground movement (geohazard), stress corrosion cracking, winkle bends, weld mismatch, buried casing, and depth of coverage prediction. Because it is a remote inspection technique, there are no limitations due to the pipeline manufacturing and installation conditions, such as curvature, weld type, thickness and diameter.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.057 |
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".