Pipeline Geohazard Target Susceptibility Threshold – A Reliability-Based Rationalization
Bibliographic record
Abstract
Abstract Pipeline geohazard assessment involves the delineation and quantification of threat severity associated with a suite of geohazard mechanisms deemed credible for a specific setting or project. The context for a typical assessment is loss of containment from the pipeline — an ultimate limit state (ULS) — considering individual geohazard mechanisms (e.g., landslide, fault displacement, rockfall, subsidence, etc.). To estimate the probability of loss of containment associated with a particular geohazard mechanism at a given location, the evaluation process can be partitioned into an estimate of the probability of occurrence of the geohazard mechanism at that location, and the conditional probability of loss of pipe integrity should the event occur. The product of these two probabilities is termed “susceptibility” expressed as loss of containment events per year at a given location. A typical approach to manage geohazards assessed in this way is to set a target susceptibility threshold to determine mitigation requirements to reduce the estimated susceptibility value for individual geohazards. The rationale for selecting a target susceptibility threshold value has been a topic of interest in recent pipeline projects in Canada. This paper demonstrates a reliability-based approach in rationalizing the selected pipeline geohazard target susceptibility threshold and linking geohazard assessment results to Quantitative Risk Assessment (QRA) of all threat categories in ASME B31-8S.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".