Case Study: Application of Best Practices for Pipeline Construction on Steep Slopes
Bibliographic record
Abstract
Abstract In 2019, Coastal GasLink started the construction of the 670 km-long 48-inch gas pipeline across the province of British Columbia (BC) Canada. The project was constructed across varied terrain, including construction across two mountain ranges, the Rocky Mountains and the Coastal Mountain Range. The final 83 km of pipeline (Section 8) is characterized by an ascent through the Coastal Mountains to the highest point on the project at 1,428 m above sea level and a descent to sea level at the LNG Canada terminal in Kitimat, BC. Pipeline Section 8 contained a number of steep slopes (SS) ranging in angle from 15 degrees to 63 degrees. Many of these steep slopes had unique features, ranging from topography, geohazards, construction windows, accessibility, and weather-related factors. Each factor contributed to the selection process for the most suitable method of steep slope pipeline construction. The steep slope pipeline construction techniques which were used consisted of: i. Conventional open cut via trenching by blasting and mechanical excavation, a. Open cut using winch-supported equipment, b. Open cut with hydraulic sled using a top-down pipeline installation sequence, ii. Raise bore of an inaccessible steep slope (SS3), and iii. Cable cranes over a steep mountain side with slope lengths of up to 1,373 m and slopes angles up to 58 degrees. The raise bore and the cable crane construction techniques were a first in Canada for pipeline installation, providing innovative and unique solutions to pipeline construction. The objective of this case study is to provide an overview of the various steep slope pipeline construction techniques, provide a discussion on the challenges and successes for each technique with a summary of the lessons learned. A summary of the safety practices developed and implemented will also be presented.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".