Bibliographic record
Abstract
Abstract Various worldwide, high profile asset integrity incidents have heightened attention to the integrity of pipelines in recent years. Setting Shell's corporate strategy for Pipeline Integrity provides interesting opportunities for capturing best practices from a diversity of operating units and environments from around the globe - from the frozen arctic like conditions of Sakhalin, to highly sour service in Canada, the tropical climates of Brunei and Malaysia, and operational challenges in Nigeria to increasingly regulated environments in the USA and North Sea. Key components of an effective Pipeline Integrity Management System (PIMS) include: Organisation/Roles and Responsibilities, Standards and Procedures, Competencies and Technical Authorities, Asset Registration, Risk Assessment, Maintenance & Integrity Work Plan, Data Management, Integrity assessment and verification, Reporting of Compliance and Integrity Status, Management of Change, Emergency Response, Reviews and Audits. This paper will discuss the framework for effective Pipeline Integrity Management within Shell to ultimately meet the aspiration of "Our [pipeline] assets are safe and we know it" and can show it to the various stakeholders.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".