Are Integrity Management Programs Making a Difference?
Bibliographic record
Abstract
As a result of numerous stress corrosion cracking incidents in the 1980s and early 1990 the National Energy Board (NEB) held an Inquiry1 in 1995 on the SCC failure mechanism and how to prevent failures. One of the recommendations of the Inquiry was Companies were to develop a SCC management program to proactively identify and mitigate SCC. Based on the apparent success of the SCC programs in significantly reducing SCC failures, the NEB revised its Onshore Pipeline Regulations in 1999 (OPR-99)2 to require companies to develop an integrity management program (IMP) for all hazards. This paper discusses the evolution of integrity management program (IMP) requirements and evaluates incident rates and other performance metrics to determine if there is evidence that IMPs have contributed to the improvement of safety of pipelines. The paper highlights the challenges associated with gathering incident and IMP performance metrics and evaluating the data to determine if there is a correlation between the implementation of IMP and pipeline safety. In addition, the analysis discusses the challenges associated with comparing data between different countries and regulatory jurisdictions. Suggestions for future improvement are identified.
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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 | 0.043 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".