Progressive Integrity Assessment Solutions using Quantitative Methods
Bibliographic record
Abstract
Abstract The paper presents a phased approach to the quantification of pipeline risk due to corrosion, SCC and other defect- based hazards. Public concern and pressure from regulatory bodies are accelerating the need for pipeline operators to formalize and intensify their approach to integrity management, and there is a growing acceptance of a risk-based approach to the prioritization, planning, and evaluation of integrity assessments. Varying amounts and quality of data are required to do risk assessments depending on the methodology used and the required outcome. For pipelines not currently on a formal assessment plan, the data available tend to be sparse and qualitative in nature. This level of data quality and availability seldom lends itself to a fully quantitative risk assessment, but is adequate for qualitative and/or semi-quantitative methods, and these methods, in turn, are adequate to address the initial prioritization of integrity assessments. As assessment data become available, the operator can take advantage of risk methodologies that progressively incorporate quantitative data. The phased quantitative methodologies described in this paper incorporate quantitative assessment data that caters to the developing needs of the operators. Most operators need a phased approach to progressively adopt the most relevant and needed assessments. These organized and relevant steps need to be introduced according to data available and maturity of the integrity management program. Each refinement to the quantification offers distinct advantages to the user. The paper explains these advantages. Corrosion is used as an example, but the approach is applicable to any time-dependent defect based hazard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".