Comparison of Feasibility of a Standard Reliability-Based Approach and a Bayesian Network Approach for Integrity Management of a Northern Canadian Liquids Pipeline
Bibliographic record
Abstract
Abstract Integrity management of a northern Canadian liquid pipeline required a unique approach due to the presence of methanol-induced, short, axially-oriented, internal stress corrosion cracking (iSCC) adjacent to girth welds. Previous studies focused on investigation of the susceptibility to iSCC, crack detection performance of inline inspection tools, and leak rates due to iSCC. The current feasibility study aimed to identify an approach to estimate the probability of leak due to iSCC that uses information from previous investigations and handles the data gaps. Two probabilistic approaches — a standard reliability-based approach and a Bayesian Network approach — were compared based on a number of criteria related to effective use of existing data, flexibility of the framework, and ability to iteratively improve the results. The results of the comparison indicate that a hybrid of the two approaches is an ideal way to develop a comprehensive integrity management tool.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".