Trans Mountain Corporation Overcomes Numerous Challenges to Construct the Trans Mountain Expansion Project
Bibliographic record
Abstract
The Trans Mountain Pipeline Expansion Project is the most largest technically challenging pipeline project ever constructed in Canada and possibly in North America. This project consists of 987 km (613 mi) of NPS 36 and NPS 42 pipeline, 11 pump stations, 3 berths, addition of 19 petroleum storage tanks, and elevation changes not normally designed for in liquid pipelines. The challenges are many and diverse. With similar challenging projects like Keystone XL being cancelled, Trans Mountain stands out in that this challenging project is under construction now with completion planned for late 2023. Challenges include environmental, regulatory, technical, geotechnical, geological, topographical, equipment, manpower, COVID-19, safety, schedule, public perception, design, and recent concerns in BC like fires, extreme temperatures, and overland flooding as well as others. Conceived by Kinder Morgan Corporation (KMC) as an expansion of its existing pipeline to expand offshore markets, ultimately the risk for the pipeline completion was such that KMC sold the pipeline to the Canadian government, essentially the people of Canada. It is very likely that once completed, Trans Mountain Corporation, the owners of the TMC system, will be sold to a major pipeline operator as the Canadian government is not in the pipeline operating business. This paper will outline where the project is now, how it got here, and how it managed all of the challenges faced.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".