Are Pipelines Being Held to an Unreasonably High Standard of Performance?
Bibliographic record
Abstract
The media and sections of the public have shown recently an acute interest in Pipeline operational performance incident statistics. Published data for North America shows that 99.999% of crude oil and petroleum products shipped by pipelines reach their destination safely. Some pipeline operators claim even better performance, 99.9996 % being one example. However, should failing to deliver 4 barrels of product for every million shipped be a legitimate cause for concern? If not how about the more general case of 1 per one hundred thousand? Is pipeline performance being singled out unreasonably when compared to other threats to public and environmental wellbeing such as medical malpractice or industrial waste contamination? Evidence from Canada and elsewhere, indicates that, during their hospital stay, an appreciable number of patients, one in every 18, experience adverse events, such as medication error, injurious falls, infections, and other medical misadventures. Errors (mostly minor), in fulfilling pharmaceutical prescriptions show an even higher error rate — 1 in 4 in one recent study, yet the public appears to be unperturbed. A common thread is determining what constitutes an acceptable level of risk whether individual or societal, voluntary or involuntary. Besides providing a broader context for pipeline risk, the paper explores the origin and intent of the environmental screening standard of 1 in 10−6, as well as the concept of setting risk tolerance to be as low as reasonably practicable — ALARP. The question of why there may be a reticence for many Pipeline Regulators to set, as other industries have, a prescriptive value for ALARP is considered.
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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".