Bibliographic record
Abstract
The number of ruptures per year is one of the National Energy Board’s (the Board) measures of safety performance of the federally regulated oil and gas pipelines. This measure was examined and analyzed over twenty, ten, and five years with respect to the rupture causes, ignitions, fatalities, injuries, pipeline age, in-line inspections, and the Board’s safety interventions. There were forty-six ruptures over the twenty-year period, twenty-three over the ten-year period, and seven over the five-year period (Ref. 1 and 2) on the 43,000 km of the regulated pipelines. The average time from the pipeline installation to the time of rupture for the time-dependent rupture mechanisms is twenty-eight years. There were three fatalities and fourteen injuries caused by the ruptures of the federally regulated pipelines over the past twenty years. Ruptures associated with fires of the gas and high vapour pressure pipelines caused most of the fatalities and injuries. The dominant rupture causes are external corrosion, stress corrosion cracking, and third-party damage in this order of magnitude. The pipelines that ruptured during the last five years were internally inspected. The in-line inspection tools could not properly detect the defects that caused the ruptures. Regulatory interventions, such as public inquires, Board Orders, and regulatory requirements, have reduced the number of ruptures due to the targeted cause. The number of ruptures and safety consequences associated with them have decreased over the last ten years.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".