Statistical Analyses of Incidents on Oil and Gas Pipelines Based on Comparing Different Pipeline Incident Databases
Bibliographic record
Abstract
Pipelines are regarded as one of the most practical and economical modes for transporting dangerous and combustible substances, such as oil and gas. However, the use of historical failure data in qualitative risk assessment of oil and gas pipelines is unusual due to lack of data or incomplete information. The pipeline incident database (PID) provides valuable information for researchers to identify potential threats of oil and gas pipeline systems, and catty out effective risk assessment. In this study, pipeline failure statistics such as pipeline classifications, incident definitions, failure causes and failure frequencies from the United States, Canada, Europe and United Kingdom are compared. Failure frequency of oil and gas pipelines for different kinds of primary failure causes are estimated from the statistical analysis of the mileage, pipe-related incident, and failure cause data collected by each PID. Although above-mentioned databases are established by pipeline operators in developed countries, the statistical analyses of incidents on oil and gas pipelines based on comparing different pipeline incident databases can benefit the quantitative risk assessment of pipeline systems also in some developing countries where pipeline incident database haven’t been established.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".