New Group VI Parallel Runway at Calgary International Airport: A Case History of Successful Implementation and Lessons Learned
Bibliographic record
Abstract
The new Parallel Runway Project (Runway 17L-35R) at Calgary International Airport (YYC) became operational on June 28, 2014. It is a new 14000 ft x 200 ft runway and dual parallel taxiway system with over 1.2 million yd2 of concrete paving and an overall investment of $590 million. The new runway provides the airport with a Group VI (ICAO Code F) CAT IIIA all-weather airport runway to satisfy the air travel accessibility and economic needs of the Calgary region with direct flight capability to Asia. The construction of a new parallel runway was planned since the early 1970s when the land was acquired by the government of Canada and land use around the airport has been controlled by the city since that time. This paper describes key milestones and events in the initial planning, program management, preliminary and detailed designs and construction management strategy as well as key implementation decisions that were adopted for the new runway. The runway work was also heavily integrated with other projects that were carried out simultaneously including the new 22-gate international terminal, new control tower and 6-lane tunnel under the runway. The integration of these projects is summarized as are the key lessons learned on the project including the initial framework study and life cycle cost analysis that set the parameters for the contracting strategy for this successful project.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".