Application of In-Line Inspection and Failure Data to Reduce Subjectivity of Risk Model Scores for Uninspected Pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pipeline risk models are used to prioritize integrity assessments and mitigative actions to achieve acceptable levels of risk. Some of these models rely on scores associated with parameters known or thought to contribute to a particular threat. For pipelines without in-line inspection (ILI) or direct assessment data, scores are often estimated by subject matter experts and as a result, are highly subjective. This paper describes a methodology for reducing the subjectivity of risk model scores by quantitatively deriving the scores based on ILI and failure data. This method is applied to determine pipeline coating and soil interaction scores in an external corrosion likelihood model for uninspected pipelines. Insights are drawn from the new scores as well as from a comparison with scores developed by subject matter experts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it