Uncertainty quantification of wall thickness of onshore gas transmission pipelines
Bibliographic record
Abstract
The present study quantifies probabilistic characteristics of the wall thickness of welded pipe joints in onshore gas transmission pipelines based on about 5900 field-measured wall thicknesses collected from a pipeline system in Canada. The collected data cover a wide range of pipe nominal wall thicknesses, from 3.18 to 16.67 mm. By considering the measurement error involved in the collected wall thickness data, statistical analyses indicate that the actual-over-nominal wall thickness ratio (AONR) follows a normal distribution with a mean of 1.01 and a coefficient of variation (COV) ranging from 1.6% to 2.2% depending on the nominal pipe wall thickness. The implications of the developed AONR statistics for the reliability analysis of corroded pipe joints are investigated. This study provides key input to the reliability-based design and assessment of pipelines with respect to various threats, such as metal-loss corrosion and stress corrosion cracking.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".