Impact of Maintenance Activities on Future Integrity of Transmission Pipelines
Bibliographic record
Abstract
Abstract Transmission pipeline operators regularly inspect their assets using in-line inspection (ILI) tools to monitor for potential internal and external threats to the system. When these tools identify features that meet excavation criteria, the operators will complete mitigation activities to reduce or remove the threat. Typically, these mitigation activities include excavation of the pipeline, removal of the coating, and non-destructive examination at the targeted feature. Upon completion of the maintenance activities, the pipeline is then re-coated and the backfill restored. During the maintenance work, the pipeline’s coating at the ends of the excavation is exposed to atmospheric conditions (e.g., sun light, humidity, etc.). Moreover, the pipeline is then exposed to disturbed soil with varying moisture content after being backfilled. Depending on the coating type, these conditions may increase the corrosivity of the localized environment at the existing coating (that was left as-is) and at the pipe ends. Approximately 16,000 digs in Canada and the United States of America were analyzed to determine the impact of maintenance activities on the future integrity of transmission pipelines. A re-visit rate at previously excavated locations of less than 1% was observed based on this analysis. Typically, the revisit occurs 4 years after the initial visit. As expected, most of the revisits were associated with pipelines that were originally coated with polyethylene tape. Within this paper, strategies to reduce the revisit rate will also be discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".