Simulating non-homogeneous non-Gaussian corrosion fields on pipelines based on inline inspection data
Bibliographic record
Abstract
This study presents a methodology to simulate non-homogeneous non-Gaussian corrosion fields on the external surface of buried steel pipelines by using inline inspection (ILI) data. It is assumed that the non-homogeneous non-Gaussian corrosion field consists of multiple homogeneous non-Gaussian anomalies that can be characterized by the marginal distribution and spatial autocorrelation function of the corresponding corrosion depths. Based on corrosion data collected from in-service pipelines, empirical relationships are developed to estimate parameters of the marginal distribution and autocorrelation function from the ILI information. To generate a synthetic corrosion field, one first generates realizations of corrosion anomalies and then merge the generated anomalies into a single non-homogeneous field by applying a spatial modulating function to each anomaly. The proposed methodology will improve the accuracy of the fitness-for-service assessment of corroded pipelines in practice as the burst capacity of the corroded pipeline can be more accurately evaluated by using the synthetic corrosion field than using the idealized corrosion field obtained from the ILI data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".