Probabilistic Bow-Tie Model to Predict Failure Probability of Oil and Gas Pipelines
Bibliographic record
Abstract
According to the 2013 Report Card, oil and gas pipelines of the United States of America are in poor condition. Pipelines are proved to be safer and more efficient than the other means of transportation of petroleum products. However, they have failed during their operation and sometimes their failures have caused catastrophic losses and serious injuries. Most of the pipelines are laid underground, thus their condition is very difficult to be evaluated. On the other hand, a comprehensive study of the previous works proves the lack of an integrated model on the failures of these pipelines. This paper aims to model the probability of failures based on the historical data on the incidents of oil and gas pipelines. After identification of the main sources of pipelines’ failures, historical data is used to build a Probabilistic Failures’ Bow-Tie Model for Oil and Gas Pipelines. The model will be able to recognize the potential failure sources of each pipeline and predict the probability of occurrence of the major hazards.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".