Implementing a Quantitative Geohazard Frequency Analysis Framework as a Component of Risk Assessment of New Pipelines
Bibliographic record
Abstract
In the last 5 years in Canada, regulators have been requesting that new pipeline projects provide quantitative risk management of all credible geohazards involving the proposed pipeline corridor so it can be demonstrated that geohazards are being recognized prioritized and that adequate resources are being allocated and to minimize the impact of adverse consequences of pipeline construction and operation. Complete risk management includes risk analysis that identifies credible geohazards sites, estimates their annual frequency or probability of pipeline failure and, when combined with a consequence of pipeline failure, estimates the risk from each hazard. This paper presents a framework and methodology that quantitatively estimates the Frequency of Loss of Containment (FLoC) for several types of geohazards that meet the requirements for geohazard identification and frequency analysis components of risk analysis. This framework builds on an international geohazard management framework advanced in the last decade by the Australian Geomechanics Society, British Columbia forestry industry, used in geohazard management programs for operating pipelines and proposed pipeline projects in Canada. The framework provides a repeatable and defensible methodology that is intended to be scalable to accept inputs from feasibility level desktop studies, through field-based observations, and incorporate proposed mitigations. This updated framework was most recently implemented on a proposed large diameter transmission pipeline route crossing the varied terrains of Western Canada, the results of which have been adjusted for Owner confidentiality, but are presented to demonstrate the application of the methodology and the effectiveness of communicating the overall hazard frequency reduction as a result of applying site specific mitigations.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".