Guidelines to Develop Fitness-for-Service Assessment of Exposed Pipeline Due to Flood Events: Investigation, Assessment and Mitigation
Bibliographic record
Abstract
Abstract Over the course of the British Columbia (BC)/ Washington State (WA) flood event, the pipeline restart preparation required a significant, sustained effort to complete a detailed assessment of the pipe’s integrity. Trans Mountain Corporation was able to develop a detailed Fitness-For-Service (FFS) assessment for the exposed pipe segments due to the flood and gain approval from the CER (Canada Energy Regulator) for a successful pipeline restart. On the evening of November 14, 2021, the Trans Mountain Pipeline LP (Trans Mountain) pipeline was shut down as a precautionary measure in response to an ongoing extreme rainfall event. There were multiple sites identified as critical pipe exposures where the pipeline was exposed and unsupported, or experienced mechanical damage, due to soil wash out. All confirmed exposure sites were reported to the CER and Trans Mountain responded in-field to support and protect the pipeline from flowing water, complete pipe inspections, determine the pipe’s FFS, and complete the required repairs before pipeline restart. This paper provides a summary of the FFS assessment that Trans Mountain completed to ensure the integrity of its oil pipeline system after the significant geotechnical event, and the process followed to develop the long-term integrity plans.
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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 | 0.036 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".