A Framework for the Probabilistic Integrity and Risk Assessment of Unpiggable 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
Abstract Pipeline integrity programs require validation techniques such as in-line inspection (ILI), pressure testing (PT), or direct assessment (DA). Many unpiggable pipelines present operational restrictions for the use of ILI tools. DA is applied through four steps: data gathering; indirect examination (IDi); detailed examination (DEx); and post-assessment. The flow and corrosion models used for the IDi are physics-based and do not quantify the uncertainties in the variables, the models, nor the corrosion process. The selection of verification sites for the DEx from the IDi, is not risk based. The post-assessment does not include a formal risk evaluation. The purpose of this paper is to present a framework for the integrity assessment of unpiggable pipelines, which are subject to internal corrosion. This integrity assessment is done by combining probabilistic flow and corrosion models with risk assessment. The flow model calculates variables that affect the corrosion process, thereby enhancing the predictability of the corrosion model. A risk analysis combines the information from the corrosion model with a consequence model to define the verification sites, and field verification is used to update the corrosion model. Risk evaluation uses the output of the risk analysis to recommend optimal inspection, maintenance, and risk mitigation strategies.
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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 | 0.001 |
| 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