Statistical analysis of failure consequences for oil and gas pipelines
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 among the safest methods to transport oil and gas, but when an incident occurs, it can lead to disaster. Pipeline failures often cause injuries, fatalities, explosions and fires due to product ignition, property damage, and spills that can lead to environmental impact. The likelihood and consequence analyses of pipeline failures from past events are necessary for the development of realistic risk models. For this reason, a statistical analysis of failure consequences between 2010 and 2015 based on the Pipeline and Hazardous Materials Safety Administration (PHMSA) database is provided in this paper. Relationships between the pipeline failure consequences and the basic pipeline design variables are investigated. They provide a valuable contribution to pipeline risk modeling. Results show that recently installed hazardous liquid pipelines of large diameters and high operating pressure are more likely to cause ignitions. In contrast, older installed hazardous liquid pipelines of small diameters cause larger release volumes and more expensive property damages. The portion of fatalities and injuries that is caused by distribution pipeline accidents is higher for the public than workers compared to other pipeline types.
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