Distribution Pipeline Risk Framework
Bibliographic record
Abstract
Abstract By nature, gas distribution is a network system; it does not fit well with traditional pipeline risk models that assume a linear geometry. Distribution system growth is multigenerational and often leads to mixed assets in the same area where transmission pipeline segments are often constructed within shorter time frames and more uniform materials. Slow, sporadic growth leads to varied record availability and quality that might not readily support commercially available risk models. The project described in this paper was initiated to develop predictive methods to prioritize mitigation and replacement activities for distribution networks. Priority is assigned to areas by risk, equipment characteristics and environmental attributes. In a previous IPC paper, the authors developed a histori-calbased predictive model but applied it to a single city area. This work has been extended to cover the entire province of Saskatchewan. The model relies on logistic regression and a machine learning algorithm to associate the historical failure rate with the asset type, age, pipe material, diameter, pressure, and an array of geographical-dependent attributes such as soil properties and climate events. The output of the model allows integrity engineers to consider predicted failure rates to complement the lagging performance indicators used to develop integrity program planning. This model demonstrates the advantage of using available distribution system records to develop a custom historicalbased predictive model. Consequence estimates for distribution networks are also described. Distribution leaks are often classified into hazard levels that differentiate operational response. These are assigned based on incident data records and SME input to develop an event tree for the consequence of a distribution leak. This paper summarizes the work performed during the project to calculate distribution asset probability of failure and consequences.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".