A Framework for Safety-Constrained Integrity Management for Gas Pipelines Using Defined Risk Acceptance Criteria
Bibliographic record
Abstract
Risk assessment has been used historically in pipeline integrity to provide relative risk rankings based on a mixture of qualitative and quantitative inputs. With the improvement of assessment and data collection techniques and technologies, and the corresponding improvement in hazard and consequence modeling that these techniques have made possible, pipeline operators are now able to calculate risk on an entirely quantitative basis. This improvement allows operators to manage pipeline integrity-related risk within a framework that allows levels of risk reduction to be related to integrity costs using comparable terms and to measure the acceptability of residual levels of risk against responsible and defensible risk acceptance criteria. The framework outlined in this paper, coupled with quantified risk models, allows pipeline operators the ability to identify areas that may require risk reduction, identify preferred risk-reduction methods, provide justification for the project, and monitor residual levels of risk. The introduction of defined risk acceptance criteria also provides operators with a tool to move beyond relative risk prioritization towards the ability to discriminate between pipe operating at acceptable integrity levels and pipe requiring risk mitigation.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".