Network Vulnerability Assessment of the U.S. Crude Pipeline Infrastructure
Bibliographic record
Abstract
The potential for cascade failure of the U.S. crude oil pipeline infrastructure is analyzed using Model Based Risk Assessment software. The pipeline system that distributes crude oil to refineries across the United States has gained much media attention with President Obamas denial of a permit to complete a key portion the Keystone-XL pipeline that will carry oil from Alberta, Canada to the Cushing Oil Trading Hub (COTH) in Cushing, OK. The analysis identified the COTH as the primary critical hub. The COTH is one of the worlds major oil terminals. A disruption of the COTH, Midwest/West Coast oil distribution networks, or critical hubs would have far-reaching negative consequences affecting global trade. The analysis also identified regional differences in network resiliency and susceptibility to cascade failure. Protecting all 55,000 miles of the U.S. crude oil pipeline infrastructure from catastrophic failure is an unachievable goal, but protection of the network from cascade failure and a Black Swan event can be achieved by protecting network hubs. The results of this analysis should be used as a starting point to increase network resiliency and prioritize the use of resources to secure the crude oil pipeline network against cascade failure.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".