Development of Probability of Ignition Model for Ruptures of Onshore Natural Gas Transmission Pipelines
Bibliographic record
Abstract
A log-logistic probability of ignition (POI) model for ruptures of onshore natural gas transmission pipelines is proposed in this paper. The parameters of the proposed POI model are evaluated based on a total of 188 rupture incidents that occurred on onshore gas transmission pipelines in the U.S. between 2002 and 2014 as recorded in the pipeline incident database administered by the Pipeline and Hazardous Material Safety Administration (PHMSA) of the U.S. Department of Transportation. The product of the pipe internal pressure at the time of rupture and outside diameter squared is observed to be strongly correlated with POI and therefore adopted as the sole predictor in the POI model. The maximum likelihood method is employed to evaluate the model parameters. The 95% confidence interval and upper confidence bound on the POI model are also evaluated. The model is validated against an independent set of rupture incident data reported in the literature. The proposed POI model will facilitate the quantitative risk assessment of onshore natural gas transmission pipelines in the U.S.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".