Statistical Analyses of Incidents on Oil and Gas Pipelines Based on Comparing Different Pipeline Incident Databases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it