Analysis of Dynamic System Risks Where Pipelines Cross Slow-Moving Landslides
Bibliographic record
Abstract
Abstract Pipelines are often constructed across dormant or normally slow-moving landslides. The potential for pipeline failure in response to landslide movement depends on several factors. These include the likelihoods of different landslide velocities being realized, the probabilities that movements physically impact the pipeline, the capacity of the pipeline to accommodate landslide displacements, and the ability of operators to detect and respond to deteriorating conditions. Each of these factors is difficult to predict but estimating the probabilities of landslide velocity transitions is particularly challenging. In this paper we review a conceptual approach to predict landslide velocity and displacement using Markov chains that combine geomorphic evidence of long-term landslide behaviour with current estimates or measurements of landslide velocity. A framework is proposed to combine time-dependent estimates of landslide displacement and the deterioration of pipeline strain capacity to estimate the probability of pipeline failure over time and in response to potential changes in landslide velocity. The expected efficacy of monitoring programs and trigger action response plans is accounted for in the vulnerability model.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".