An Emerging Methodology of Slope Hazard Assessment for Natural Gas Pipelines
Bibliographic record
Abstract
TransCanada Pipelines Ltd. (TransCanada) operates approximately 37,000 km of natural gas gathering and transmission pipelines. Within the Alberta portion of this system there are almost 1100 locations where the pipeline(s) traverse slopes, primarily as the line approaches and exits stream crossings. In the past, the approach to managing the impact of slope movements on pipeline integrity has been reactive; site investigations and/or monitoring programs would only be initiated once the slope movements were sufficiently large so as to easily observe cracking or scarp development. In some cases these movements could lead to a pipeline rupture. To move to a proactive hazard management approach and to optimize the maintenance expenditure, TransCanada has developed a new slope assessment methodology. The objective of this methodology is to establish a risk-ranked list of slopes upon which maintenance decisions can be based. Using only internal and public information on site conditions as input to predictive models for rainfall-ground movement and pipe-soil interaction, a probability of pipeline failure can be generated for each slope. Estimates of risk using a consequence-matrix approach enabled the compilation of a risk-ranked list of hazardous slopes. This paper describes this methodology, and its implementation at TransCanada, and presents some of the results.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".