Enbridge Cost and Schedule Contingency Assessments
Bibliographic record
Abstract
All capital projects have an element of risk and uncertainty. In today’s business environment this requires more than just simply adding 10 percent contingency to the cost estimate to cover off project unknowns. Before sanctioning a project for hundreds of millions of dollars, Board of Directors need to know what possible cost and schedule outcomes exist in order to safe-guard shareholders’ investments. Contingency assessments must be: • Risk-based • Project Specific • Repeatable • Defendable • Cost effective Six years ago, Enbridge grappled with these issues and realized it needed to adopt a new method of assessing both cost and schedule contingencies. After evaluating options, Enbridge set upon developing an in-house parametric modeling solution for its contingency assessment needs. This paper will: • Identify various options for assessing contingency • Review the Enbridge process • Demonstrate the value of a simplified risk register • Identify required data inputs • Illustrate calibration and accuracy of assessments • Discuss business advantages of parametric modelling
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".