Bibliographic record
Abstract
This paper gives an overview of the history of oil and gas pipelines, and their status today. It concludes with a glimpse at future pipelines, and issues we will be facing as we continue to burn fossil fuels and look for alternatives. Pipelines have been used for thousands of years, but modern day pipelines have their origins in Pennsylvania, USA in the mid-1800s. As technology improved, larger and longer pipelines were built, and the demand for energy during World War II increased both the need and extent of pipelines in the USA, then around the world. Most countries now have large oil and gas pipelines systems: Russia has huge pipeline networks, and if you laid the Canadian pipeline system, end to end, it would extend 17 times around the world! Worldwide, there are about 3,500,000km of transmission pipelines transporting oil and gas. The big issue facing this vast pipeline system today is age; for example, over 50% of the 1,000,000 km USA oil and gas pipeline system is over 40 years old. The continuing demand for oil and gas will mean these ageing systems will need to function safely and efficiently for many more years. Therefore, the future for our current pipelines will see an emphasis on inspection and maintenance. But what about new pipelines? Certainly, we will be building many new oil and gas pipelines, some in hostile environments, such as deep water. We will also be building pipelines to carry differing products, such as carbon dioxide and hydrogen, as the drive for cleaner and alternative fuels continues.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".