Multi Tiered Approach to Slope Movement Management: Case Study
Bibliographic record
Abstract
A major slope in southern Manitoba is currently experiencing deep seated movements of approximately 60mm per year. This 20m high × 70m long slope contains a pipeline right of way with five large diameter crude oil lines that were constructed from 1950–1998. It is estimated that the slope has moved over 3 meters since the pipeline installations. Management of the effects of this slope movement on the pipelines has involved cross-functional strategies that include geotechnical, integrity, and stress evaluations. The slope is assessed annually by a geotechnical engineer, and the most likely cause for the slope movements has been determined. Slope monitoring equipment has been installed at key locations and is monitored at frequent intervals. A toe berm has been installed to prevent lower slope failure at the creek bed that is located at the slope toe. A finite element stress analysis, which considers the interaction between the soil movement and pipeline, has been generated. This stress analysis evaluated the pipeline stresses due to the slope movements to date, and also due to a possible sudden mass movement. The results are backed up by a bending strain analysis based on inertial in-line inspection data was conducted for several of the lines. This paper presents an overview of the engineering assessment considering structural, material, geotechnical and operational concerns involved in developing an integrity management action plan.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".